Apparatus for internetworked hybrid wireless integrated network sensors (WINS)

ABSTRACT

The Wireless Integrated Network Sensor Next Generation (WINS NG) nodes provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS NG network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. The WINS NG nodes combine microsensor technology, low power distributed signal processing, low power computation, and low power, low cost wireless and/or wired networking capability in a compact system. The WINS NG networks provide sensing, local control, remote reconfigurability, and embedded intelligent systems in structures, materials, and environments.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/158,013, filed Oct. 06, 1999, U.S. Provisional Application No.60/170,865, filed Dec. 15, 1999, U.S. Provisional Application No.60/208,397, filed May 30, 2000, U.S. Provisional Application No.60/210,296, filed Jun. 08, 2000, U.S. patent application Ser. No.09/684,706, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,565, filed Oct. 4, 2000, U.S. patent application Ser. No.09/685,020, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,387, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,490, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,742, filed Oct. 4, 2000, U.S. patent application Ser. No.09/680,550, filed Oct. 4, 2000, U.S. patent application Ser. No.09/685,018, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,388, filed Oct. 4, 2000, U.S. patent application Ser. No.09/684,162, filed Oct. 4, 2000, and U.S. patent application Ser. No.09/680,608, filed Oct. 4, 2000, all of which are incorporated byreference.

GOVERNMENT LICENSE RIGHTS

The United States Government may have certain rights in some aspects ofthe invention claimed herein, as the invention was made with UnitedStates Government support under award/contract number DAAD 16-99-C-1024issued by US AMCAC NATICK Contracting Division.

BACKGROUND

1. Field of the Invention

This invention relates to the field of intelligent networks that includeconnection to the physical world. In particular, the invention relatesto providing distributed network and Internet access to sensors,controls, and processors that are embedded in equipment, facilities, andthe environment.

2. Description of the Related Art

Sensor networks are a means of gathering information about the physicalworld and then, after computations based upon these measurements,potentially influencing the physical world. An example includes sensorsembedded in a control system for providing information to a processor.The Wireless Integrated Network Sensor (WINS) development was initiatedin 1993 under Defense Advanced Research Projects Agency (DARPA) programsupport. The Low-power Wireless Integrated Microsensors (LWIM) programpioneered the development of WINS and provided support for thedevelopment of fundamental low power microelectro-mechanical systems(MEMS) and low power electronics technology. The LWIM program supportedthe demonstration of the feasibility and applicability of WINStechnology in defense systems. See: K. Bult, A. Burstein, D. Chang, M.Dong, M. Fielding, E. Kruglick, J. Ho, F. Lin, T.-H. Lin, W. J. Kaiser,H. Marcy, R. Mukai, P. Nelson, F. Newberg, K. S. J. Pister, G. Pottie,H. Sanchez, O. M. Stafsudd, K. B. Tan, C. M. Ward, S. Xue, J. Yao, “LowPower Systems for Wireless Microsensors”, Proceedings of InternationalSymposium on Low Power Electronics and Design, pp. 17-21, 1996; J. G.Ho, P. R. Nelson, F. H. Lin, D. T. Chang, W. J. Kaiser, and O. M.Stafsudd, “Sol-gel derived lead and calcium lead titanate pyroelectricdetectors on silicon MEMS structures”, Proceedings of the SPIE, vol.2685, pp. 91-100, 1996; D. T. Chang, D. M. Chen, F. H. Lin, W. J.Kaiser, and O. M. Stafsudd “CMOS integrated infrared sensor”,Proceedings of International Solid State Sensors and ActuatorsConference (Transducers '97), vol. 2, pp. 1259-62, 1997; M. J. Dong, G.Yung, and W. J. Kaiser, “Low Power Signal Processing Architectures forNetwork Microsensors”, Proceedings of 1997 International Symposium onLow Power Electronics and Design, pp. 173-177, 1997; T.-H. Lin, H.Sanchez, R. Rofougaran, and W. J. Kaiser, “CMOS Front End Components forMicropower RF Wireless Systems”, Proceedings of the 1998 InternationalSymposium on Low Power Electronics and Design, pp. 11-15, 1998; T.-H.Lin, H. Sanchez, R. Rofougaran, W. J. Kaiser, “Micropower CMOS RFcomponents for distributed wireless sensors”, 1998 IEEE Radio FrequencyIntegrated Circuits (RFIC) Symposium, Digest of Papers, pp. 157-60,1998; (Invited) G. Asada, M. Dong, T. S. Lin, F. Newberg, G. Pottie, H.O. Marcy, and W. J. Kaiser, “Wireless Integrated Network Sensors: LowPower Systems on a Chip”, Proceedings of the 24th IEEE EuropeanSolid-State Circuits Conference, 1998.

The first generation of field-ready WINS devices and software werefielded in 1996 and later in a series of live-fire exercises. TheLWIM-II demonstrated the feasibility of multihop, self-assembled,wireless network nodes. This first network also demonstrated thefeasibility of algorithms for operation of wireless sensor nodes andnetworks at micropower level. The original WINS architecture has beendemonstrated in five live fire exercises with the US Marine Corps as abattlefield surveillance sensor system. In addition, this firstgeneration architecture has been demonstrated as a condition basedmaintenance (CBM) sensor on board a Navy ship, the USS Rushmore.

Prior military sensor systems typically included sensors with manualcontrols on sensitivity and radio channel selection, and one-waycommunication of raw data to a network master. This is wasteful ofenergy resources and inflexible. In the LWIM network by contrast,two-way communication exists between the sensor nodes and the master,the nodes contain signal processing means to analyze the data and makedecisions on what is to be communicated, and both the communications andsignal processing parameters can be negotiated between the master andthe sensor nodes. Further, two-way communications enables considerationof more energy-efficient network topologies such as multi-hopping. Thearchitecture is envisioned so that fusion of data across multiple typesof sensors is possible in one node, and further, so that the signalprocessing can be layered between special purpose devices and thegeneral-purpose processor to conserve power. The LWIM approach to WINSrepresented a radical departure from past industrial and military sensornetwork practice. By exploiting signal processing capability at thelocation of the sensor, communications energy and bandwidth costs aregreatly reduced, allowing the possibility of scalably large networks.

The DARPA sponsored a second program involving both UCLA and theRockwell Science Center called Adaptive Wireless Arrays for InteractiveReconnaissance, surveillance and target acquisition in Small unitoperations (AWAIRS), whose genesis was in 1995. Its focus has been uponthe development of algorithms for self-assembly of the network andenergy efficient routing without the need for masters, cooperativesignal processing including beamforming and data fusion across nodes,distributed self-location of nodes, and development of supportinghardware. A self-assembling network has been demonstrated. Moreover, theAWAIRS program includes notions such as layered signal processing ofsignals (including use of multiple processors within nodes, as in LWIM),and data aggregation to allow scaling of the network. A symposium washeld in 1998 to discuss the implications of such sensor networks for awide variety of applications, including military, health care,scientific exploration, and consumer applications. The AWAIRS nodes havealso been used in condition based maintenance applications, and have amodular design for enabling various sensor, processing, and radio boardsto be swapped in and out. There is now a confirmed set of WINSapplications within the Department of Defense for battlefieldsurveillance and condition based maintenance on land, sea and airvehicles, and WINS technology is being considered as a primary land minereplacement technology. See: J. R. Agre, L. P. Clare, G. J. Pottie, N.P. Romanov, “Development Platform for Self-Organizing Wireless SensorNetworks,” Aerosense '99, Orlando, Fla., 1999; K. Sohrabi, J. Gao, V.Ailawadhi, G. Pottie, “A Self-Organizing Sensor Network,” Proc. 37thAllerton Conf. on Comm., Control, and Computing, Monticello, Ill.,September 1999; University of California Los Angeles ElectricalEngineering Department Annual Research Symposium, 1998; K. Yao, R. E.Hudson, C. W. Reed, D. Chen, F. Lorenzelli, “Blind Beamforming on aRandomly Distributed Sensor Array System,” IEEE J. Select. Areas inComm., vol. 16, no. 8, October, 1998, pp. 1555-1567.

There are also a number of commercial sensor technologies that arerelated to WINS, in that they include some combination of sensing,remote signal processing, and communications. Some of these technologiesare described herein, along with some expansion upon the specificfeatures of LWIM and AWAIRS.

FIG. 1 is a prior art control network 100. The network 100 typicallyincludes sensors 102, a master 104, and possibly a plurality ofactuators 106 that are tightly coupled, a configuration that results ina low delay in the feedback loop. Typically, the sensors 102 haveparameters that are controlled by the master 104. The network mayinclude a number of controllers and actuators. Results of actuation aredetected by the sensors 102, which, together with the logic in themaster 104, results in a control loop. Typically, raw measurements areforwarded to the master 104 with little or no processing (e.g., low passor passband filtering). The master 104 reports the results to a computernetwork 108. Furthermore, the master 104 accepts new programming fromthat network 108.

FIG. 2 is a prior art sensor network 200. The typical network includes anumber of sensor nodes 202, a master 204, and a user interface 206. Themaster 204 is often just another sensor node, or may be a moresophisticated device. The elements of the network 200 are handregistered, and there is limited self-assembly and reconfigurationcapability residing in the network 200 (e.g., updating of addresses asnew nodes are registered). Typically, the parameters of the sensors 202are controlled by the master 204, and raw measurements are forwarded tothe master 204 with little or no processing. For example, in remotemeter reading applications the meter value at some particular time issent. However, in LWIM networks extensive processing is performed tomake decisions, and thus reduce the communications traffic and relievethe burdens of the master. The master 204 reports the results to theuser interface 206, following some computation, using a long rangecommunication link 208. The limitation that inheres is that theinterface 206 allows for downloading of new programming (for example, ona laptop computer) via the master 204. In a typical military ormeter-reading system however, there is only one-way communicationupwards from the sensor 202 to the master 204, and thus no tuning ofnode parameters is possible.

FIG. 3 is a prior art AWAIRS sensor network 300. The sensor nodes 302 ofthe AWAIRS network 300 include extensive signal processing in order toreduce communications. The sensor nodes 302 can include multipleprocessors of differing types, and can progress through several levelsof signal processing in performing target detection and identification.The sensor nodes 302 can also include ranging devices for positionlocation. Moreover, the sensor nodes 302 enable cooperative behaviorssuch as data fusion, beamforming, and cooperative communications. Thenetwork 300 is self-organizing, and will establish routing to minimizeenergy consumption. Multihop routing is supported. The network 300 doesnot require long-range links, but can include them, and may directlyconnect to a computer and user interface 306. Moreover, the sensor nodes302 may interact with a number of user interfaces 306. Data aggregationmay be included in a path from the remote sensors to an end destination.

FIG. 4 is an example of a prior art sensor network 400 using distributedsignal processing. Source 1 emits a signal that is detected by sensors1, 2, and 3. Sensor node 1 can become designated as a fusion center towhich some combination of data and decisions are provided from sensornodes 2 and 3. Sensor node 1 then relays the decision towards the enduser using a specific protocol. Source 2 emits a signal that is detectedby sensor node 4. Sensor node 4 performs all processing and relays theresulting decision towards the end user.

Sensor node 6 receives the signals emitted by both sensors 1 and 4.Sensor node 6 may pass both decisions or perform some furtherprocessing, such as production of a summary activity report, beforepassing information towards the end user. The end user may requestfurther information from any of the sensor nodes involved in processingdata to produce a decision.

FIG. 5 is an example scenario for self-organization in a prior-artsensor network such as AWAIRS. In the limit of short hops thetransceiver power consumption for reception is nearly equal to that oftransmission. This implies that the protocol should be designed so thatradios are off as much of the time as possible, that is, the MediaAccess Controller (MAC) should include some variant of Time-DivisionMultiple Access (TDMA). This requires that the radios periodicallyexchange short messages to maintain local synchronism. It is notnecessary for all nodes to have the same global clock, but the localvariations from link to link should be small to minimize the guard timesbetween slots, and enable cooperative signal processing functions suchas fusion and beamforming. The messages can combine health-keepinginformation, maintenance of synchronization, and reservation requestsfor bandwidth for longer packets. The abundant bandwidth that resultsfrom the spatial reuse of frequencies and local processing ensures thatrelatively few conflicts will result in these requests, and so simplemechanisms can be used.

To build this TDMA schedule, the self-organization protocol combinessynchronism and channel assignment functions. It supports node-to-nodeattachment, node-to-network attachment, and network-to-networkattachment. The distributed protocol assigns progressively less of theTDMA frame to invitations and listening as the network becomes moreconnected. The result is contention-free channel assignments for thesensor nodes in a flat (peer-to-peer) network, where the channelsconsist of some combination of time and frequency assignments.Invitation slots are allocated even when the network is mature to allowfor reconfiguration.

Upon construction of the set of links, the routing is then built. If thenodes are powered by batteries, the network will have a life-cycle whichbegins in a boot-up, proceeds through a phase of maximum functionality,decline, and finally failure. Every bit that is exchanged hastens theend of the network. Particular nodes may be more heavily stressed bytraffic than others (e.g., those in the vicinity of a gateway or otherlong-range link). Thus, routing protocols must to some extent beenergy-aware, to sustain useful operation as long as possible. Theminimum energy path is not necessarily the most desirable. Rather routesare ordinarily chosen to extend operation, although high prioritymessages may be routed for low latency, even if this exhausts preciousnetwork resources. The predictability of flow to and from a relativelysmall number of gateways enables infrequent construction of sets ofpaths to these data points, minimizing overhead.

FIG. 6 is an example scenario of self-location in a prior art sensornetwork. In this scenario, sensor nodes 2, 5, 8, and 9 contain anabsolute position reference mechanism, for example Global PositionSystem (GPS) or hand registration of position. Furthermore, all sensornodes include transducers and receivers for radio frequency (RF) oracoustic ranging. As such, the network elements are homogeneous exceptpossibly for nodes 2, 5, 8, and 9, as these nodes provide position andtiming reference. Sensor node algorithms estimate ranges to neighboringsensor nodes using a time difference of arrival (TDOA) scheme. Theresults are used to set up either linear or non-linear systems ofequations using either distributed or centralized algorithms. Forexample, if all nodes can hear the four references, standard GPSalgorithms can be used independently by each node. If nodes can onlyhear near neighbors, iterative procedures may be employed. Using thissystem, a position determination is made when a sensor node hears atleast four neighboring sensor nodes. While four nodes are required foran absolute position determination in a three-dimensional system,results are better when more than four nodes are detected. Also, only asmall percentage of the sensor nodes of a network are required to makean absolute position determination.

FIG. 7 is an example of sensor/internet connections in a prior artsensor network 700. The sensor nodes 702 may be cameras, interfaced to acomputer by means of an electronic card. The interface card 704 allowsfor control by a computer 706 of a limited number of parameters. Thenetwork interface 708 includes, for example, a modem card in a computer,telephone line access, or access to an Internet Service Provider (ISP).The images processed by the host computer 706 can be viewed remotely byusers with similar Internet access, for example when the images areplaced on a publicly available World Wide Web (web) site. The imagesplaced on a web site may be downloaded and modified using remotecomputers 714 and interfaces 712 with web site access. While thisnetwork makes use of standard software, it requires an expensiveinterface between the computer 706 and each sensor node 702.Furthermore, manual configuration of the connection and the software istypically required to attach each sensor node 702 to the network 710.

As another example, in a prior art system designed for airport security,a seismic sensor and energy detector circuit is used to trigger adigital camera under the control of a computer. The image and seismicrecord are conveyed by wireless means to another computer, and fromthere posted to a web site. The trigger level can be controlled remotelyvia the web site. However, only one remote unit is supported, with nonetworking of multiple sensors, and with the requirement of a costlyinterface platform, or computer, at both ends.

While these examples indicate some aspects of wireless sensor networktechnology, many desirable features are absent. Each of these systemseither lacks ease of use, ability to use standard development tools toextend them, and/or ability to operate in variable or hostileenvironments. For example, the wireless communications technique may bevulnerable to jamming or interference, or the platform may consume toomuch energy for long-term remote operation, or it may lack simpleconnectivity to the Internet or support for database services, or onlysupport a limited number of sensing modes.

Wireless network technology has progressed so that the WINS platform orset of platforms is required to support standard operating systems anddevelopment environments, and be capable of being easily integrated intolarger networks. Only in this fashion can the physical world beseamlessly connected to the many resources available through theInternet and other networks. In particular, the WINS platforms arerequired to provide a familiar and convenient research and developmentenvironment. The cumbersome embedded systems of past implementations arenot appropriate for this next generation of progress. The customoperation systems developed for past generations of low power sensornodes have an inconvenient development environment and are not supportedby the familiar, high productivity, powerful, development tools neededby the research and development community. Furthermore, conventionalapproaches would yield a system where a platform operating with aconventional embedded operating system would require excessive operatingpower. This prevents developers from facing and solving the challengesof low power system design. The development of these essentialcapabilities requires a fundamentally different WINS node and networkarchitecture.

SUMMARY

The Wireless Integrated Network Sensor Next Generation (WINS NG) sensorsand nodes provide distributed network and Internet access to sensors,controls, and processors that are deeply embedded in equipment,facilities, and the environment. The WINS NG network is a new monitoringand control capability for applications in such sectors astransportation, manufacturing, health care, environmental monitoring,and safety and security. Wireless Integrated Network Sensors combinemicrosensor technology, low power signal processing, low powercomputation, and low power, low cost wireless (and/or wired) networkingcapability in a compact system. The WINS NG networks provide sensing,local control, and embedded intelligent systems in structures,materials, and environments.

The WINS NG networks provide a more efficient means of connecting thephysical and computer worlds. Sensor nodes self-organize to form anetwork, and seamlessly link to the Internet or other external networkvia a gateway node, which can be of the same type or different from thesensor nodes. The sensor nodes can themselves be of the same type or avariety of types. Network resources such as databases are available tothe sensor network and the remote user through the Internet or otherexternal network.

The sensor nodes are constructed in a layered fashion, both with respectto signal processing and network protocols, to enable use of standardtools, ease real-time operating systems issues, promote adaptability tounknown environments, simplify reconfiguration, and enable lower-power,continuously vigilant operation. High reliability access to remote WINSNG nodes and networks enables remote interrogation and control of thesensor network; this reliability is achieved using a plurality ofcouplings, with automatic adjustment of the processing andcommunications to deal with failures of any of these couplings. Linkageto databases enables extra resources to be brought to bear in analysisand archiving of events, and database methods can be used to control theentire network in a more transparent manner, to enable more efficientcontrol and design.

The WINS NG technology incorporates low-energy circuitry and componentsto provide secure communication that is robust against deliberate andunintentional interference, by means for example of new algorithms andantenna designs. The network can further include distributed positionlocation functionality that takes advantage of the communications andsensing components of the individual nodes, to simplify deployment andenable location of targets.

The sensor nodes can be of a variety of types, including very simplenodes that may, for example, serve as tags. These nodes can beconstructed on flexible polymer substrates, a material that may be usedfor a wide variety of synergistic uses. This construction results inmore compact and capable systems, providing sensors, actuators,photo-cells and structural properties. Compact antennas for suchpackages have been developed. The network includes both wireless andwired communications capability, using a common protocol andautomatically choosing the more secure or lower power mode when it isavailable, providing more robust and long-lived operation in potentiallyhostile environments. The network enables a wide variety of users withdifferent data rate and power requirements to coexist as, for example,in wired or wireless mode vehicular applications. The flexibility of thedesign opens a wide variety of applications.

In another aspect, the layering of the WINS nodes with respect toprocessing and signal processing facilitates the rapid design of newapplications. Layering further facilitates self-organization of completeapplications, from network connections through to interoperation withremote databases accessed through external networks such as theInternet. With this layering, the cost of deployment is radicallyreduced even while remote operation is enabled.

The descriptions provided herein are exemplary and explanatory and areintended to provide examples of embodiments of the claimed invention.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures illustrate embodiments of the claimedinvention. In the figures:

FIG. 1 is a prior art control network.

FIG. 2 is a prior art sensor network.

FIG. 3 is a prior art Adaptive Wireless Arrays for InteractiveReconnaissance, surveillance and target acquisition in Small unitoperations (AWAIRS) sensor network.

FIG. 4 is an example of a prior art sensor network using distributedsignal processing.

FIG. 5 is an example scenario for self-organization in a prior-artsensor network such as AWAIRS.

FIG. 6 is an example scenario of self-location in a prior art sensornetwork.

FIG. 7 is an example of sensor/internet connections in a prior artsensor network.

FIG. 8 is an embodiment of a WINS NG network.

FIG. 9 is another embodiment of a WINS NG network.

FIG. 10 is a block diagram of WINS NG operation of an embodiment.

FIG. 11 is a block diagram of processing within a WINS NG sensor node ofan embodiment.

FIGS. 12 and 13 show browser screen images or pages associated withremote Internet operation of a WINS NG node of an embodiment.

FIG. 14 is a browser screen image of an embodiment including an acquiredimage.

FIG. 15 is a block diagram of a WINS NG node of an embodiment thatenables rapid development of high performance signal processingapplications, while preserving low-power operation.

FIG. 16 shows the WINS NG architecture partitioning of an embodiment.

FIG. 17 is a block diagram of a WINS NG application programminginterface (API) of an embodiment.

FIG. 18 is a block diagram of a distributed system application of anembodiment.

FIG. 19 is a block diagram of a Sensor Interface Processor (SIP) of anembodiment.

FIG. 20 is a WINS NG node of an alternate embodiment.

FIG. 21 is a WINS NG network architecture of an alternate embodiment.

FIG. 22 is a multicluster network architecture supported in anembodiment.

FIG. 23 is a bandwidth budget for a distributed sensor network of anembodiment.

FIG. 24 shows a WINS NG data frame format of an embodiment.

FIG. 25 is an example of self-configuration in a WINS NG network of anembodiment.

FIG. 26 is another example of self-configuration in a WINS NG network ofan embodiment.

FIG. 27 is one protocol for radios that are establishing links in anembodiment.

FIG. 28 is another protocol for radios that are establishing links in anembodiment.

FIG. 29 shows a single radio cluster.

FIG. 30 shows a multi-cluster network.

FIG. 31 shows another multi-cluster network.

FIGS. 32-34 show a network self-assembly example of an embodiment.

FIG. 35 is a block diagram of a WINS NG gateway of an embodiment.

FIG. 36 is a WINS NG network architecture of an embodiment havingreliable access to remote networks.

FIG. 37 is an example scenario of Internet access and databasemanagement of an embodiment.

FIGS. 38A and 38B are a diagram of a process flow of a state machine ofan embodiment.

FIG. 39 is an example scenario of network element self-location in asensor network of an embodiment.

FIG. 40 is a PicoWINS node architecture of an embodiment.

FIG. 41 is a hybrid network including PicoWINS nodes of an embodiment.

FIG. 42 is a PicoWINS node of an embodiment.

FIG. 43 is a block diagram of a PicoWINS node of an alternateembodiment.

FIG. 44 diagrams a security system using a WINS NG sensor network of anembodiment.

FIG. 45 shows a deployment network architecture of a WINS NG sensornetwork of an embodiment.

FIG. 46 is a multihop network architecture of a WINS NG sensor networkof an embodiment.

FIG. 47 shows an example of WINS NG system shielding by distribution inspace in an embodiment.

FIG. 48 shows an example of WINS NG system shielding by network routingin an embodiment.

FIGS. 49A and 49B show an example of WINS NG system shielding bydistribution in frequency and time in an embodiment.

FIG. 50 is an asset management architecture including the WINS NG orPicoWINS tags of an embodiment.

FIG. 51 is a diagram of a vehicle internetworking system of anembodiment.

FIG. 52 is a WINS NG network of an automotive embodiment.

DETAILED DESCRIPTION The Wireless Integrated Network Senson NextGeneration (WINS NG) Technology

The Wireless Integrated Network Sensor Next Generation (WINS NG) nodesof an embodiment combine sensing, signal processing, decisioncapability, and wireless networking capability in a compact, low powersystem. Recent advances in integrated circuit technology enableconstruction of far more capable sensors, radios, and processors at lowcost, thereby enabling mass production of sophisticated systems thatlink the physical world to networks compact geometry and low cost allowsWINS NG to be embedded and distributed at a small fraction of the costof conventional wireline sensor and actuator systems.

WINS NG is a fundamental advance for network access to densely anddeeply distributed sensing, control, and processing systems.Applications for WINS NG extend from a local scale to a global scale.For example, on a local, wide-area scale, battlefield situationalawareness provides personnel health monitoring and enhances security andefficiency. On a local, enterprise scale, WINS NG creates amanufacturing information service for cost and quality control. On alocal machine scale, WINS NG condition-based maintenance devices equippower plants, appliances, vehicles, and energy systems with enhancedreliability, reductions in energy usage, and improvements in quality ofservice. On a national scale, transportation systems, and borders can bemonitored for efficiency, safety, and security. Also, on a metropolitanscale, new traffic, security, emergency, and disaster recovery servicesare enabled by WINS NG. In the biomedical area, WINS NG connectspatients in the clinic, ambulatory outpatient services, and medicalprofessionals to sensing, monitoring, and control. On a global scale,WINS NG permits environmental monitoring of land, water, and airresources. It is, thus, fundamentally a technology that efficientlylinks networks to the physical world.

WINS NG is provided in one embodiment in a scalable, low cost, sensornetwork architecture that conveys sensor information to the user at alow bit rate with low power transceivers. Continuous sensor signalprocessing is provided to enable constant monitoring of events in anenvironment. Thus, for all of these applications, local processing ofdistributed measurement data is used for a low cost, scalabletechnology. Distributed signal processing and decision making enableevents to be identified at the remote sensor. Thus, information in theform of decisions is conveyed in short message packets. Futureapplications of distributed embedded processors and sensors will requiremassive numbers of devices. Conventional methods for sensor networkingpresent impractical demands on cable installation and network bandwidth.By reducing the requirements for transmission of measured data, the WINSof described embodiments reduce the burdens on communication systemcomponents, networks, and human resources.

The WINS NG network devices support local sensing and control withresponse requirements ranging from real-time through latency tolerantprocesses. A function of WINS NG networks is supporting constantlyvigilant signal processing and event recognition associated with thissensing and control. Furthermore, WINS NG systems support applicationsat multiple tiers. For example, the applications that includegeographically wide distribution of WINS NG technology support longrange wireless communication links. In contrast, applications in factoryautomation or health care are supported using local area networks. Inthese applications, as will be described herein, WINS NG networksexploit the advantages of short range, robust, multihop wirelessnetworks.

The deployment of WINS NG networks for a variety of applications isenabled by the low cost, scalable, self-installing architecture ofembodiments described herein. The scalability of a WINS NG network isprovided by powerful local computation ability, adaptation to theenvironment, remote reconfigurability, and communication security.Multiple users interact with the WINS NG network, monitor and controlthe network, and query for events, locations, data, and configurationvia the Internet. The design of such systems formerly required expertiseranging from sensing and radio communication to high level networkabstractions. Relatively few design teams possess all the necessarycapabilities. This disadvantage of prior systems is addressed by anembodiment of WINS NG that enables designers to work mainly at thelevels above the data link layer, with standard software tools.

FIGS. 8 and 9 show embodiments of a WINS NG network. The networkincludes nodes 802, gateway nodes 804, server 806, and web assistants ornode control web or browser pages (not shown), but is not so limited.The sensor nodes 802 include any combination of actuators, sensors,signal processors, energy or power supplies, data storage devices,wireless communication devices, wireline communication devices, andself-location capabilities. The sensor nodes 802 are distributed in anenvironment 899 that is to be monitored or controlled. The network caninclude heterogeneous elements. Local users 830 may interact, ifauthenticated, with the network via the nodes 802 themselves through alocal display and user interfaces (UIs). Non-local users can interactwith the network through gateways 804. Thus, connections to servers 806,database services 820, and other network resources are available, anduser 832 can access the network with standard tools. The user or clientcomputer can access the WINS network continuously or intermittently, andmay interface via processors of vastly different capabilities accordingto a particular application (e.g., personal computers, personal digitalassistants (PDAs), or bidirectional pagers). A complete sensor networkmay, in one embodiment, be viewed as a distributed but active databasethat answers questions about the physical world, and acts upon thequestions through the actuators. Multihop communication permits lowpower operation of dense WINS sensor networks.

The network architecture of FIGS. 8 and 9 is self-organizing withrespect to an ability to distribute some combination of information andenergy. The network interacts with remote users 832 and databases 820when coupled to the Internet 810 or other networks using a gateway 804.The WINS node data is transferred over the possibly asymmetric wirelesslink to an end user 832 or to a conventional wireless network service,for example an Internet Protocol network 810, through a WINS gateway 804or a network bridge. Internetworking provides remote accessibility viaweb-based tools to data (e.g., signals and images), code (e.g., signalprocessing, decision support, and database elements), management (e.g.,node and network operation), and security functions.

The sensor nodes of an embodiment are remotely programmable.Furthermore, software is downloadable from storage locations in thesensor node network, or via the Internet from remote user locations ordatabases. Moreover, results or data products of sensor nodes may beremotely queried. Additionally, the network is capable of supportingdistributed processing and data storage functions in accordance withvarying sensor node capabilities and application demands.

FIG. 10 is a block diagram of WINS NG operation of an embodiment.Operation begins by coupling the WINS NG network elements or nodes to anenvironment to be monitored 1002. Data is collected from the monitoredenvironment using some combination of the WINS NG network elements ornodes 1004. The functions of the network elements or nodes are remotelycontrolled/manipulated or programmed by a user through a client computer1006. The client computer can include a number of processing devicesfrom portable processing devices like personal digital assistants,pagers, or personal computers to servers. Information is distributedamong the WINS NG network elements or nodes 1008, information thatincludes, but is not limited to, raw data collected, processed data, andnode resource information. Data processing is distributed among variouscombinations of the WINS NG network elements or nodes in response to thenode resource information 1010.

The descriptions herein include physical embodiments of the nodes,signal processing architecture, network architecture, methods forensuring reliability of access, linkage to databases, security methods,and position location functions.

Signal Processing Architecture

A requirement in a security application is constant vigilance by atleast a subset of the sensors, so that all threats are detected.However, the most sophisticated detection algorithm does not need to runall the time. A low false alarm or misidentification probability isdesired along with a high detection probability. This is provided usingqueued data and energy-efficient procedures and/or algorithms. Simplealgorithms of this type are well-suited to dedicated processors. Energythresholding and limited frequency analysis on low-sampling ratemagnetic, acoustic, infrared, or seismic sensors are in need of suchsolutions, having both low circuit complexity and clock rates. Havingpassed this test, other sensors that consume more energy can be turnedon, and higher levels of processing and fusion invoked.

FIG. 11 is a block diagram of processing within a WINS NG sensor node1100 of an embodiment. The sensor node 1100 includes various signalprocessing, sensing, information storage, energy supply, andcommunications capabilities. Support is provided for multiple layerswithin individual sensor nodes 1100 and also for multiple layers amongdifferent sensor nodes 1100. Physical interfaces and software interfacesallow plug and play interoperability by all classes of node devices.Consequently, processing may be distributed across node devices in thesame category, or across node devices in multiple categories.

Particular application program interfaces (APIs) are provided that allowfor higher level programming of this distributed processing. Forexample, APIs are provided for control of sensor nodes, actuators,communications, special purpose processors, information storage, andenergy management. Moreover, the APIs permit downloading of newinstructions to control these operations. In an embodiment, resourceusage parameters flow up through the network from the physical layer tothe application layer. Furthermore, parameters that set priorities fornetworking behavior including signal processing, data transfer, datastorage, and data aggregation flow down from the application to thephysical layer. These parameters apply to many levels of the network,and the API framework of an embodiment makes it convenient to operate atany subset of these levels.

The preprocessor 1104 of an embodiment is a hardware device thatfacilitates the separation between lower level programming and higherlevel programming, while also permitting lower power operation. Thepreprocessor 1104 is coupled between at least one high level processor1102 and devices or functions of the network that are linked to thephysical world and require real-time operation. The devices or functionsof the network that are linked to the physical world include, but arenot limited to, the sensor suite 1106, the communication devices 1108,the signal processors and storage devices 1110, the power or energysupplies 1112, and the actuation suite 1114. Each node may include anumber of combinations and variations of the sensor suite 1106, thecommunications devices 1108, the signal processors and storage devices1110, the power supplies 1112, and the actuation suite 1114.

At some point in processing, a threshold may be crossed whereby a sensornode seeks information from nearby sensors, for purposes of data fusionor coherent beamforming. Because communication of raw data is verycostly in terms of energy, this should occur later in the processingchain. Additionally, the processing requirements at this level are verylarge. Ultimately, a classification decision might be made using a largeneural network or some other equally computationally hungry procedurefor situations in which less sophisticated processing is unable toprovide an answer with the required degree of certainty. In the worstcase, raw data may be hopped back to a remote site where a human beingperforms analysis including pattern recognition.

Two points emerge regarding processing. First, networks of nodes of anembodiment exploit the probabilities of the events of interest in orderto process only to the extent required. Most of the time, there are notargets, and thus no need to apply the most expensive algorithm in termsof processing expense. Also, there will be too many circumstances inwhich the least expensive algorithm will fail. A processing hierarchyleads to huge cost reductions while assuring the required level ofreliability. Second, the processing hierarchy is intertwined withnetworking and data storage issues. How long and where data is queueddepends on location in the processing hierarchy. Whether a nodecommunicates and to what set of neighboring nodes depends on the signalprocessing task that is required. The communications costs in turnaffect the processing strategy (e.g., willingness to communicate, andwhether processing is centralized or distributed). All of this rests onphysical constraints, and therefore the physical layer intrudes upthrough to applications.

Indeed, it is through exploitation of the application that low-powerdesign becomes possible. It is not necessary to perform general purposesignal processing in all of the nodes, nor do the networks need to begeneral-purpose and high-bandwidth. FIGS. 12 and 13 show browser screenimages or pages associated with remote Internet operation of a WINS nodeof an embodiment.

In this system, the WINS node includes two sensors with seismic andimaging capability, but is not so limited. The seismic sensor isconstantly vigilant, as it requires little power. Simple energydetection is used to trigger the operation of the camera. The image andthe seismic record surrounding the event are then communicated to aremote observer. In this way, the remote node need only perform simpleprocessing at low power, and the radio does not need to supportcontinuous transmission of images. The networking allows human ormachine observers to be remote from the scene, and allows archivalrecords to be stored. The image data allows verification of events, andis usually required in security applications that demand a humanresponse. Both the seismic record 1202 and 1312 and an image creatingthe record 1204 and 1314 are shown. FIG. 12 shows a vehicle 1204, andFIG. 13 shows a running individual 1314.

The WINS node and WINS gateway node control web pages permit direct andremote control of event recognition algorithms via the WINS network andthe Internet. For example, the seismic energy threshold for triggeringan image is controllable remotely. WINS NG node control and managementweb pages, respectively, provide networking, communication, sensorsignal processing, and sensor interface reconfigurability. A userwishing to modify the WINS NG protocols, node code and data objects, andto deploy new code libraries does so using the WINS NG Web Assistant,but is not so limited.

When using a visible WINS imager, the browser screen image displays auser interface with an image acquired from the camera. FIG. 14 is abrowser screen image 1400 of an embodiment including an acquired image1402. The user interface 1404 of an embodiment allows the user to zoomand pan through the image, as well as to apply image processingfunctions. In an alternative embodiment, the number of imagestransmitted is further reduced with an increased sensor suite ofshort-range detectors (e.g., infrared or magnetic), or by adding moresophisticated processing within the nodes. Different applications demandquite different solutions, many of which are accommodated within thehierarchical processing framework. For example, images might be queuedat nodes pending decisions from groups of sensors. With the addition ofsatellite communication interfaces, the sensor network may be located atany point on the globe and the remote client access and control mayoccur from any other point and with a large number of users.

FIG. 15 is a block diagram of a WINS NG node of an embodiment thatenables rapid development of high performance signal processingapplications, while preserving low-power operation. The WINS processor1502, is a low power, low cost, conventional microprocessor system witha well-supported standard operating system platform. The WINSpreprocessor 1504 is a low power system that includes sensing, signalprocessing, communication, and platform management hardware andfirmware. In addition to the sensing and communication functions, theplatform management capability enables the WINS preprocessor 1504 tomanage the WINS processor 1502 power and operation maintaining low dutycycle, and therefore, low energy.

FIG. 16 shows the WINS NG architecture partitioning of an embodiment.The WINS NG architecture partitioning provides critical advantages foroperating power and performance, rapid software system development, andupgrade capabilities. First, the WINS preprocessor platform managementcontrol enables the WINS node to selectively operate in a state ofcontinuous vigilance 1602. For continuous monitoring, the WINSpreprocessor operates its micropower sensing, and signal processing, andactuator control front end continuously. However, the WINS preprocessoralso manages the WINS Platform 1604 and enables its cycling into and outof a power-down state. This enables the WINS processor to operate at lowduty cycle 1604. The preprocessor also provides supervisory functionsfor the processor and memory systems, providing additional levels ofrobustness, and also supervising radio and other communications systemsoperation. The WINS processor is available when required, for thecomputationally intensive functions of signal identification,cooperative behaviors, database management, adaptation, reconfiguration,and security functions. By moving these functions to a general purposeprocessor, software development is eased. In another embodiment, some ofthe more frequently invoked of these functions can be performed byspecial purpose processors, to reduce power consumption at the cost ofreduced flexibility.

By encapsulating the sensing, communication, and platform functions, theWINS NG architecture also provides a critical advantage for development.In conventional single processor architectures, the WINS developer wouldbe faced with the requirements of managing real-time sensing andactuation threads while operating in the background on essential highlevel functions. The WINS NG architecture enables the developer tochoose between multiple paths. For example, the developer may focuscompletely on high level information technology research and developmentwhile exploiting full access to the physical world via open interfaces.Alternatively, the developer may choose to build low level applications,(in addition to high level systems) by working through the WINSpreprocessor open interfaces. By encapsulating real-time functiondetails, development resources may be focused on delivering the mostvaluable software information technology products.

The WINS NG architecture permits multiple upgrade and modificationpaths. The preprocessor includes standard interfaces including serialinterfaces. This permits the preprocessor to be used with the processor(a Windows CE platform in one embodiment), or a wide range of otherplatforms, for example, open source embedded operating systems or Linux.The WINS NG node can be upgraded or modified by substituting theprocessor component or upgrading only the preprocessor.

An embodiment of the WINS NG gateway includes a WINS NG Radio Frequency(RF) modem. In this gateway, the WINS NG sensing functions are replacedby network gateway functions for the interface between the low powerdistributed sensor network and a 10 Mbps ethernet 10 baseT networks, asdescribed herein. Other embodiments of the WINS NG gateway can includeaccess and support of a plurality of wired networks, long range tacticalradio networks (e.g. satellite communication), and/or access totelephony.

FIG. 17 is a block diagram of a WINS NG API 1700 of an embodiment. TheWINS NG API provides the capability for development of tactical sensingapplications with the WINS NG Platform. The API includes sensing 1702,signal processing 1704, communication 1706, platform control 1708, andnetworking, but is not so limited. The WINS NG API receives resourceusage parameters from lower levels or physical layers of the network.Furthermore, parameters that set priorities for signal processing,information routing, and resource usage through the network flow downfrom the API layer to the physical layers. Thus, the WINS NG APIframework makes it convenient to operate an any subset of these levels.

FIG. 18 is a block diagram of a distributed system application 1800 ofan embodiment. The tactical sensor application provides novel sensing1802, signal processing 1804 and event detection 1806 methods that relyon cooperative sensing exploiting database technologies for non-localevent correlation. In addition, networking methods relying on methodsincluding directed diffusion 1808 are supported, by reporting physicalparameters such as link quality of service, energy costs, messagepriority, and other variables related to computational or communicationsresources through the WINS NG network using the APIs 1810. The completedistributed system application of WINS NG combines this technology withremote server and remote user access systems.

A description of individual components and functions of the WINS NG APIof an embodiment follows.

a. BOOL Gate_Acquisition: Initiates data acquisition by a SensorInterface Processor. Sampled data is transferred to a data buffer in thepreprocessor. Member variables include: Initial measurement gain (whichmay be later updated by automatic gain control (AGC) if AGC is enabledby SET_AGC), channel select, sample rate, and number of samples. Thisfunction is used for continuous or burst sampling of data with signalprocessing and other functions operating in the preprocessor. This isappropriate for low power operation in a continuously vigilant state.

b. BOOL Gate_Streaming_Acquisition: Initiates data acquisition by thesensor interface processor. Sampled data is transferred to a data bufferin the processor. Member variables include: Initial measurement gain(which may be later updated by AGC if AGC is enabled by AGC_SET),channel select, sample rate, and number of samples. This function isused for continuous or burst sampling of data with signal processing andother functions operating in the processor. This is appropriate fordevelopment, testing, and operation in a high performance alarm state.

c. BOOL Set_AGC: Enables and configures Automatic Gain Control (AGC)state. Member functions and variables include: Enable AGC, high levelamplitude threshold, low level amplitude threshold, sampling windownumber of samples, and filter settings. This function configures the AGCsystem. A high level threshold value determines the time-averagedamplitude level of input signals above which a lower input gain value isapplied. A low level threshold value determines the time-averagedamplitude level of input signals below which a higher input gain valueis applied. Time averaging of signals is set by the sampling windownumber of samples. The high and low level thresholds may be equal, orseparated to create a hysteresis in operation to avoid frequent andunnecessary changes in gain value. A filter may be applied to inputsignals to focus AGC control on a particular frequency band.

d. BOOL Set_Alarm_Trigger: Enables and configures the Alarm Triggerfunction state. Member variables include: high level amplitudethreshold, low level amplitude threshold, sampling window number ofsamples, and filter settings. This function configures the Alarm Triggersystem. A high level threshold value determines the time-averagedamplitude of input signals above which a Trigger signal is generated. Alow level threshold value determines the time-averaged amplitude levelof input signals below which a Trigger Signal is generated. Timeaveraging of signals is set by the sampling window number of samples.The high and low level thresholds may be equal, or separated to create ahysteresis in operation to avoid frequent and unnecessary changes ingain value. A filter may be applied to input signals to focus AlarmTrigger attention on a particular frequency band. The Alarm Triggersignal may be used to initiate operation of WINS NG platform operationsdue to the receipt of an input signal amplitude excursion. Use of thisfunction permits algorithms to control power status and reaction topotential threat-induced signals.

e. BOOL Transfer_Data_Buffer: Initiates transfer of preprocessor databuffer to processor. Member variables include channel select. Thisfunction is used for transfer of buffered sensor data stored in thepreprocessor. An example application of this function is the acquisitionof data streams occurring prior to an alarm condition.

f. BOOL GPS_CONFIGURE: Enables and opens a configuration channel to aGPS device. Configuration commands include the standard NMEA (NationalMarine Electronics Association) GPS function calls.

g. BOOL GPS_COMMAND: Opens a command and data acquisition channel to aGPS device. Command and data acquisition commands include the standardNMEA (National Marine Electronics Association) GPS function callsincluding: UTC Time, Latitude, Longitude, Course over ground, and groundspeed.

h. BOOL SPECTRUM_ANALYZER: Computes power spectral density (PSD) ofsensor data time series record of length 2^(N). Variable window choices,and PSD averaging are selectable.

i. BOOL FIR_GEN: Generates or computes FIR filter coefficients forspecified filter characteristics.

j. BOOL FIR_Filter: Operates on sensor data time series input andcomputes filtered output according to FIR filter coefficients.Coefficients may be stored, communicated to the WINS NG node, orcomputed locally.

k. BOOL INITIALIZE_WINS_RF_MODEM: Initializes RF modem operation andsets RF modem configuration. Member variables and functions include:Addressing mode selection (Unicast or Broadcast), Packet retransmissionattempt count, Master or Slave mode selection, and RF Section Enable.Packet retransmission attempt count is the number of automaticretransmission attempts in the event of packet errors. In the Masteroperating mode, the node controls the frequency hopping pattern for allparticipating nodes within reception range. In the Slave mode, the nodeacquires and follows the hopping pattern of a Master. Radio frequencySection Enable allows control of receive and transmit RF sections. Thisis useful for power management of communication functions.

l. BOOL NODE_IN_RANGE: Indicates whether the node received signalstrength is at a level sufficient to support link to the gateway.

m. BOOL RECEIVE_DATA: Returns array with current RF modem receive databuffer values. Includes source address of received packet.

n. BOOL TRANSMIT_DATA: Transmits data buffer to RF modem and initiatescommunication to remote node or gateway.

o. BOOL Node Search: Initiates search of network for participating nodesthat are in range of and have been acquired by the local gateway.

p. BOOL Node_Cluster Report: Returns list of node addresses and gatewayaddress for local node cluster

q. BOOL WINS_Modem_Power_Control: Sets WINS RF Modem power state(selections include full power, standby, and power off). This is appliedfor power management of the RF Modem in TDMA networks.

r. BOOL Modem_GPS_Power_Control: Sets power state of the GPS device.This is applied for implementation of low duty cycle clock or positionupdates.

s. BOOL Processor_Power_Control: Sets power state of the processor. Thisfunction enables the processor to enter a suspend state (e.g., at 0.1percent of full power) for a specified period. This function alsoprovides a supervisory reboot capability for the processor.

These APIs also enable preprocessor control of the processor, in theform of a software watchdog. The processor operating system can failduring operation due to application software errors that are notapparent at compile time. To ensure a robust and reliable platform, inan embodiment a software watchdog is run on the preprocessor thatperiodically sends “ping” commands to the processor. If the processordoes not respond with an acknowledgement within a specific time periodas configured by the application programmer, the preprocessor has theability to reboot the processor.

The APIs further enable programming of the preprocessor in moreconvenient forms than assembly code. For example, hosted on theprocessor, WINS Basic is a macro language that supports programming ofthe preprocessor at a very high level. It provides a mechanism thatenables execution of numerous code modules that pre-exist on thepreprocessor, thereby hiding the challenges of real-time programming atthe preprocessor level. WINS Basic can handle platform management,communication, networking and sensing, or it can be extended to fitcustom requirements, as more experienced software developers can enhanceor create new software modules for the preprocessor.

The language operates by mapping processor level functions topreprocessor level software modules. Executing a WINS Basic function onthe processor generates a command that is sent to the preprocessor,which in turn calls the appropriate software module. Support for passingparameters to such a module is also provided. Collectively, a sequenceof processor level functions forms a WINS Basic program that executes onthe preprocessor and features unique behaviors desired by the processorlevel programmer.

WINS Basic programs run in two basic fashions. First, as WINS Basicfunctions are called on the processor, the preprocessor simultaneouslyexecutes corresponding software modules. Second, a series of functionson the processor are mapped into preprocessor commands and sent to thepreprocessor collectively. The preprocessor stores this information as aWINS Basic program in dynamic memory. A programmer wishing to launch aprogram simply calls a run function on the processor that instructs thepreprocessor which WINS Basic program it should execute.

The layered architecture together with the APIs of the WINS NGembodiment thus enable developers to work at the level of the processor,the preprocessor, or both, making use of a variety of development tools.In particular, in an embodiment, developers may make use of the widelyavailable and low-cost Microsoft Windows CE™ tools. These include: MSVisual Studio™ including MS Visual C++™, MS Windows CE Toolkit forVisual C++™, and the Microsoft Developers Network (MSDN) subscriptionwhich provides valuable references. The WINS NG Development Platform canbe a conventional PC with the Windows NT Workstation™ or Windows NT™operating system. Development may be done using the Windows CE platformemulator provided in the Toolkit above. Also, development may beaccomplished via serial (and ethernet) link to the processor. TheToolkit includes program upload and remote diagnostics tools including aremote debugger, process viewer, and other tools. When working at thislevel, developers are shielded from real-time operator system concerns,and may program in high-level languages for greater efficiency.Additionally, operating systems may be supported, with the developmentof the appropriate APIs.

The preprocessor development is optional. The WINS NG API supportsaccess to all sensing, communication, and platform control functions.Other embodiments provide API upgrades that add additionalfunctionality. One available tool for development at the preprocessor inthis embodiment is Dynamic C™ from Z-World.

Regarding the electronics employed in an embodiment of the WINS NGnodes, the sensors and sensor interfaces meet the specifications ofdevices used in the leading Department of Defense reference tacticalsensor data acquisition systems. These sensor systems meet or exceed theperformance of the tactical sensors that are currently in the field. Thethreat detection sensors include seismic, acoustic, and infrared motiondevices. Global Positioning System (GPS) modules are included as wellfor location and time references. In this embodiment of the WINS NGnode, up to four sensors may be attached, with software controllingwhich sensor is sampled. Each WINS NG node carries a GPS receiver.Sensors may be located near the WINS NG node but need not be locateddirectly on or in the WINS NG package. This provides critical deploymentflexibility for optimally locating the tactical sensor becausefrequently the typical node package is not optimally located forseismic, acoustic, or infrared motion sensing. Examples of sensors thatcan be used include, but are not limited to: geophones for seismicdetection; infrared sensors based on pyroelectrics; and compact electetmicrophones for acoustics. The GPS unit supports standard NationalMarine Electronics Association (NMEA) protocols.

In an embodiment, the WINS NG preprocessor is a multiprocessor systemthat includes a Sensor Interface Processor (SIP) and a ControlProcessor. FIG. 19 is a block diagram of a Sensor Interface Processor(SIP) 1900 of an embodiment. Sensors are coupled to the WINS NG SensorInterface Processor (SIP) 1900, which operates as a component of thepreprocessor. The SIP 1900 includes sensor preamplifiers, anti-aliasingfilters, analog multiplexers, data converters, digital buffers, anddedicated processors. The SIP 1900 ensures the ability to acquiresynchronous sampled sensor data. In an embodiment, the analog input hasvariable gain, the anti-aliasing filter is programmable for differentsampling rates, an RS232 Serial port is provided for GPS (or other usesif GPS is not present), and there is a digital input/output with 8uncommitted digital lines.

The control processor can be implemented using a low power processorsuch as the Z180, supplemented by flash memory and static random accessmemory (SRAM), three serial ports at the SIP 1900, and a real timeclock. This allows the preprocessor to exercise functions such as wakeup for the processor, so that the latter can be in sleep mode with highduty cycle, conserving energy.

The processor can be any of a number of commercial platforms, such asthe Uniden PC-100 or equivalent. It is supplemented by random accessmemory (RAM) and read-only memory (ROM). In one embodiment, theprocessor system also includes a serial RS-232 port, Compact Flash Slot,user interfaces in the form of Display, Touch Screen, Microphone, AudioOutput, and employs as its Operating System Windows CE 2.0. If the nodeis to serve as a gateway, it can include a gateway Ethernet interface,using for example a compact flash card Ethernet network interfaceadapter, supporting NE2000 Compatible 10 base T Ethernet Interface (IEEE802.3), using twisted pair cables, a standard RJ-45 8-pin femaleconnector, and interfacing to the Compact Flash slot at the WINS NGprocessor.

The WINS NG system can operate with a constantly vigilant preprocessorsampling all sensor data. The preprocessor is responsible for dataacquisition as well as alert functions. Specifically, in the event thata threshold excursion is observed, the preprocessor detects and alertsthe processor. The processor, which may have been operating in a sleepstate, is now available for signal processing and event identification.Further, high level functions including cooperative detection, databasetransactions and other services may now be negotiated by the processor.At all times, the algorithms may be implemented to minimize powerdissipation.

The layering of the processing functions in the WINS NG system enablescontinuous vigilance at low power operation, while preserving theability for more sophisticated signal processing on node, and the use ofstandard software tools for the development of these higher levelfunctions. At the same time, the software interfaces allow access to thelevels more deeply connected to the physical world so that the completeprocessing stack can be tuned. The layering of processing functionsextends well beyond the nodes, through cooperative processing amongnodes in the sensor network, and through connections to externalnetworks.

Collaborative processing can extend the effective range of sensors andenable new functions while being pursued at many levels of processing.For example, consider target location. With a dense array, one method oftracking target position in an embodiment is for all nodes which detecta disturbance to make a report. The centroid of all nodes reporting thetarget is one possible estimate of the position of the target; theresponses of the nodes might alternatively be weighted according toreported signal strength or certainty of detection. Relatively few bitsof information are exchanged per node with this target location method.

More precise position estimates can be achieved in an embodiment bybeamforming, a method that exchanges time-stamped raw data among thenodes. While the processing is relatively more costly, it yieldsprocessed data with a higher signal to noise ratio (SNR) for subsequentclassification decisions, and enables estimates of angles of arrival fortargets that are outside the convex hull of the participating nodes. Twosuch clusters of nodes can then provide for triangulation of distanttargets. Further, beamforming enables suppression of interferingsources, by placing nulls in the synthetic beam pattern in theirdirections. Thus, although beamforming costs more in signal processingand communications energy than the simplified location estimator, itprovides additional capabilities.

Another use of beamforming is in self-location of nodes when thepositions of only a very small number of nodes are known. The trackingand self-location problems are closely connected, and it is possible toopportunistically locate nodes that would otherwise provide auxiliaryinformation to a target location operation. Thus, targets are used toprovide the sounding impulses for node location. Depending on theapplication, it might be advantageous to use sparse clusters ofbeamforming-capable nodes rather than a dense deployment ofless-intelligent nodes, or it may be advantageous to enable both sets offunctions. For example, a sparse network of intelligent nodes can beoverlaid on a dense network of simpler nodes, enabling control of thesimpler nodes for collection of coherent data for beamforming. Thesimple nodes might be data collection boxes with flow control capabilityand limited decision making power; other capabilities are built on topof them by adding appropriate processing and control node networks.Those skilled in the art will realize that there are many architecturalpossibilities, but allowing for heterogeneity from the outset is acomponent in many of the architectures.

A WINS NG node can include a radio frequency (RF) modem. In anembodiment, the RF modem includes a frequency hopped spread spectrumsystem operating in the unlicensed 2.4 GHz industrial, scientific andmedical (ISM) band. Specifications for this RF modem include BinaryFrequency Shift Keying (BFSK) modulation, frequency hopping among 50channels, and programmable addressing of 6 byte IEEE 802.3 addresses.The modem operates in a master/slave hierarchy where the master modemprovides synchronization for many slave modems. By default, the gatewaymodem may function as a master. However, this is not required nor alwaysoptimal. The master/slave hierarchy can be exploited for design ofmultihop networks, as described herein, using the ability to promote amodem from a slave to a master state or demote it from a master to aslave state.

In one embodiment, the WINS NG node is contained in a sealed, waterproofsystem. The package contains the WINS preprocessor, processor, andsensors. Sensors may also be deployed externally to the package,particularly in the case of acoustic and infrared motion sensors. Whileequipped with rechargeable batteries, a battery eliminator is includedwith WINS NG for operation during development.

FIG. 20 is a WINS NG node 2000 of an alternate embodiment. This node2000 provides high performance analog sensor sampling, sensor signalprocessing, wireless network support, a 32-bit application processor anda POSIX-compliant real-time operating system. The node platform includesa real-time interface processor (RTIP) 2002 that supports high-speedmulti-port sampling integrated with both a high speed DSP 2004 anddirect digital I/O 2006. The RTIP 2002 together with the associated DSPs2004 and control processors 2008 constitutes the preprocessor of thenode. The architecture also includes a 32-bit application processor 2010with RAM, ROM, and flash memory. Digital I/O and GPS geolocationcapability is provided with a coupled active antenna. The wirelessnetwork interface includes an adaptive dual mode RF modem system 2012that provides a solution for scalable, multihop networking with spreadspectrum signaling.

The analog sensor interfaces 2005 include two sets of interfaces, butare not so limited. One set provides sampling rates from 1-25 kHz at12-bit resolution, and the second set provides sampling from 1.88 to101.1 Hz at 16-bit resolution, both with selectable gains. This providessupport for a wide range of sensors. The sensor front-end high-speedinput sample rate is accommodated in a power-efficient approach with adedicated programmable digital signal processor (DSP) 2004, for examplethe industry standard Texas Instruments 5402. This DSP is supplied withan integrated development environment. The DSP code may be communicatedto the platform via a developer port or directly via the wirelessnetwork.

The application processor 2010, for example, the Motorola Power PC MPC823 Rev A supplemented by 16 MB RAM and 8 MB flash memory, supports theQNX Neutrino POSIX-compliant, microkernel real time operating system(RTOS). QNX follows the detailed standards set for modern UNIX systems.QNX provides C++ development with STL support as well as Java languagesupport from IBM/OTI. Thus, applications can be readily constructed, andcapability is provided for conveniently porting software among nodes.Alternatively, an embedded Linux may be used as the operating system.

Integrated within each node is a dual mode RF modem 2012. The modems2012 can be integrated into a scalable multi-cluster, multi-hop network.The new dual mode approach solves the long-standing problem thatrestricts most commercial spread spectrum modem solutions to localcluster/star networks. Here, the modem is a significant advance in thatit may simultaneously join two clusters. The system operates in the2.4-2.4835 GHz ISM band transmitting at 100 mW or 10 mW on dualchannels, using frequency hopped spread spectrum with transmission ratesup to 56 kbps on each channel.

The network is self-assembling to adapt to any deployment configurationin which node-to-node connectivity is established. The system alsoprovides wireline interfaces with both 10 Mb Ethernet and R-232 serialport access.

Node development can be conducted through the node Ethernet port 2020 oran R232 diagnostic port 2022. With the QNX Neutrino operating system,standard UNIX tools are available at no cost facilitating softwaredevelopment. Development may be performed in a self-hosted Neutrinoenvironment, on a workstation running QNX. The node can mount a filestructure on a remote machine, and development and file transfer isfacilitated by the nodes capability to run telnet, tftp, and other filetransfer protocols. For development, IBM/OTI Java with IDE,Microanalyzer, and other tools are available.

The WINS NG node of an alternate embodiment is constructed using amodular software framework. This framework enables the development ofsoftware that is modular, reusable, and portable across platforms.

Software developed within this framework is composed of modules. Amodule is a piece of software that presents one or more clearly definedinterfaces and maintains internal state. The framework defines astandard form for these interfaces so that modules can be reused bychanging the inter-module connections. Interfaces between modules aredefined by the types of data they send and receive and, loosely, by thecommands they accept. Any two modules that support compatible interfacescan be connected together, enabling activation and data to flow from onemodule to the other.

For example, consider a framing layer for a network device. The “upper”interface of the module sends and receives packets of data to be framedand sent. The “lower” interface sends and receives buffers of serialdata containing framed data packets. The module itself performs theframing and deframing function: buffers arriving from below are parsedto extract data packets which can be sent upwards, while packetsarriving from above are composed into frames and sent down. If a systemusing this framing module needed to change its framing algorithm, analternative framing scheme could be implemented in a module withidentical interfaces and swapped in with no other coding requirements.Modules can even be swapped in dynamically at runtime.

The modular framework is implemented as a single-threaded system with ascheduler, but is not so limited. Before the system is started, it isconfigured. Module interfaces are coupled together and are registered asevent handlers. When the system is running, it waits in the scheduleruntil an event occurs. Events can be timer expirations or I/O eventssuch as a file descriptor becoming readable or writable. When one ofthese events occurs, the appropriate handler function is called, andthis activation propagates through the network of coupled modules, doingwork along the way, and returning error codes on the way back.

Activation and data is passed through module interfaces using callbackfunctions that conform to standard semantics. This interface is definedby three functions:

int recv(void *self, DataType data)

int unblock(void *self)

int ctrl(void *self, int function, void *args, int nbytes, int *rbytes)

The recv( ) callback gets called when there is new data that is to bepushed to the module. This method is sometimes called a “push” model.Data arriving at the edges of the system is immediately pushed throughthe system until it is sent out of the system, consumed, or buffered.The recv( ) callback may return EOK to indicate success, EAGAIN toindicate that the data could not be processed at that time and is to besent again at a later time, or another error code to indicate an errorcondition.

The unblock( ) callback gets called when the module coupled to thisinterface previously refused to accept a pushed message and is nowrequesting that the message be sent again. In response to this request,the unblock( ) callback attempts to push more data through the interfaceby invoking the other module's recv( ) callback.

The ctrl( ) callback gets called when there is an out-of-band controlmessage that is to be sent to the module. The implementation of thiscallback checks the function code, and if it is a function that themodule supports, causes some effect. Arguments may be included in apointer to an argument buffer, as defined by the implementation.

For convenience, there are three standard methods of the moduleinterface of an embodiment that call through the interface to thecallback functions on the other side. These functions are:

int Send(DataType data)→calls recv( )

int UnblockSend( )→calls unblock( )

int Ctrl(int function, void *args, int nbytes, int *rbytes)→calls ctrl()

Therefore, a module can cause data to flow through associated interfacesto the module on the other side using the Send( ) call. If buffer spaceopens up, enabling the module to accept new data, it can signal thiswith UnblockSend( ), which causes any buffered data to be sent. Ctrl( )can be used to send a control message through the interface to the othermodule.

The implementation of modules in an embodiment is formulated as a statemachine. The implementation of callbacks and other code within a moduledoes not wait or block. If a delay is required as part of a sequence ofsteps, a timer is set and the call returns with no delay. The sequencethen continues, invoked from a timer callback. In some cases, theoutcome of a process cannot be known immediately. In these cases, themessage invoking the process will include a callback that is calledasynchronously to return a result when the process is complete.

In some cases, the state-machine implementation, while efficient, may becumbersome. For instances where the task at hand is better implementedprocedurally, an independent thread can be encapsulated within theframework. This thread can then make blocking I/O calls through anadaptation layer that connects to the standard module interface scheme.

The nodes of an embodiment are implemented within QNX Neutrino, but arenot so limited. The scheduler implementation masks the developer fromthe system-specific details of timers and de-multiplexing asynchronousI/O events. The inter-module interface also matches quite well to theinter-process file interface defined for device drivers in POSIXsystems. This makes it relatively easy to change the location of processboundaries in the system, without changing the design of individualmodules. The system-specific layer can also be ported to other operatingsystems, and can be remoted over a network interface.

Wins NG Network Architecture

In contrast to conventional wireless networks using voice and dataprotocols to support communication over long ranges with link bit ratesover 100 kbps, the WINS NG network supports large numbers of sensors ina local area with short range and low average bit rate communication.The bit rate of an embodiment is less than 1-100 kbps, but is not solimited. The WINS NG network design services dense sensor distributionswith an emphasis on recovering environment information. The WINS NGarchitecture, therefore, exploits the small separation between WINSnodes to provide multihop communication.

One embodiment of the WINS NG network uses multihop communication toyield large power and scalability advantages. For example, RFcommunication path loss has been a primary limitation for wirelessnetworking, with received power, P_(REC), decaying as transmissionrange, R, as P_(REC)∝R^(−α) (where α varies from 3-5 in typical indoorand outdoor environments). However, in a dense WINS network, multihoparchitectures permit N communication link hops between N+1 nodes. In thelimit where communication system power dissipation (receiver andtransceiver power) exceeds that of other systems within the WINS node,the introduction of N equal range hops between any node pair reducespower by a factor of N^(α−) in comparison to a single hop system.Multihop communication, therefore, provides an immediate advance incapability for the WINS narrow bandwidth devices. By extension, WINS NGmultihop communication networks permit large power reduction and theimplementation of dense node distribution.

The WINS NG architecture design addresses the constraints on robustoperation, dense and deep distribution, interoperability withconventional networks, operating power, scalability, and cost. Robustoperation and dense, deep distribution benefit from a multihoparchitecture where the naturally occurring short range links betweennodes are exploited to provide multiple pathways for node-to-node,node-to-gateway, and gateway-to-network communication. The WINS NGgateways provide support for the WINS NG network and access betweenconventional network physical layers and their protocols and the WINS NGphysical layer and its low power protocols. Multihop communication alsoenables low power operation by reducing range and exploiting thepower-law dependence of received RF signal strength on transmissionrange. The reduction in link range afforded by multihop communication isof particular benefit to the WINS NG applications that are tolerant ofcommunication latency. Communication latency in the WINS NG network is,in turn, tolerable due to the inherent latency associated with theresponse of conventional networks. The reduction in link range isexploited in WINS system design to provide advantages that may beselected from the set of: reduced operating power, improved bit rate,improved bit error rate, improved communication privacy throughreduction of transmit power, simplified protocols, and reduced cost.

FIG. 21 is a WINS NG network architecture of an alternate embodiment.Within this architecture nodes 2102 are coupled to gateways 2104 usingrepeaters 2106. Furthermore, mobile nodes 2112 are coupled to gateways2104 using interrogators 2116. The gateways 2104 are coupled to anetwork 2120 through a server 2130 hosting server applications 2132. Thenetwork serves to couple the nodes to an information service provider.

An important capability provided by the WINS technology is enabling avast, scalable number of sensors to maintain real-time, local contactwith the physical world. This is accomplished with access both from thedistributed sensor nodes to remote users (such as data centers), andfrom remote users to nodes. A critical characteristic for distributedsensor networks is the ratio of bits processed at the sensor interfaceto bits communicated to the user. In such situations, there is no neatseparation of signal processing and networking. A figure of merit is theinformation content per bit. Distributed sensor systems may be scalableonly if information technology is applied at the node, gateway, andserver to permit large numbers of sensors (e.g., 10³-10⁶) to communicatewith relatively few data centers (e.g., 10¹).

At the sensor node, the information per bit value may become very low.Specifically, in the case of surveillance, in the circumstance that noevent, or threat, is present, the bits produced by the sensor interfacemay carry only background noise. It is optimal in this case if thesensor data is processed, a decision is reached at the node, and a shortsummary message indicating nominal status is forwarded through thenetwork. While the sensor interface may be sampling at 10 kbps, theactual rate of communication via the network may, on average, only be0.01-1 bps. This same consideration applies to condition basedmaintenance where monitoring of equipment may be continuous, whereas therate of failures may be very low.

Bandwidth in the distributed sensor network of an embodiment ispreserved for scalability and energy reasons. In addition, bandwidth isconserved for the migration of code and data objects for management andcontrol. It is critical to note the data sources and bottlenecks wherebandwidth considerations apply. Sensors and data acquisition are thedata sources. Typical sampling rates are 1 Hz to 25 kHz in variousembodiments of WINS NG. This data is only rarely propagated directlythrough the network. Rather, information processing is applied to reducedata sets and recognize events that occur in the environment, forexample, using the layered sequence of operations described previously.After identification, only an agreed upon identifying code (e.g., acodebook pointer) need be propagated through the network.

Sensor node-to-gateway communication is a constraining bottleneck formultiple reasons. These include power constraints associated with nodeprocessing and RF communication power, and power and processingconstraints at the gateway where information from many nodes mayaggregate. The gateway to the remote monitoring site can also be abottleneck if connected, for example, by low-speed telephony throughland-line or satellite. In some embodiments the gateway can link one ormore high speed networks, but manage the links using low speed longrange connections.

FIG. 22 is a multicluster network architecture supported in anembodiment. In this multicluster architecture, nodes 2202 are dispersedin an environment with local communication 2204 between nodes 2202 andgateways 2206. Long range communication 2208 occurs between the gateway2206 and a remote data user site 2210 (e.g., using a high power RFmodem). In this embodiment, robust, frequency hopped spread spectrumtransceivers are employed. The WINS NG RF modems operate in amaster/slave hierarchy where the master modem provides synchronizationfor many slave modems. By default, the gateway modem may function as amaster. However, this is not required nor always optimal.

Multihop routing occurs between the clusters 2220 and 2222 that aredefined by the current status of the RF modems. Here, two star networks2220 and 2222 are joined. Nodes from these two networks may beintermingled in space (they are shown as separate networks, forclarity). Note that any modem in the group may perform as a master or aslave RF modem. The configuration shown can vary frequently according tooperational requirements of the network and the arrival or departure ofnodes.

Scalability challenges in this architecture include constraining thedata transfer rate between nodes for reasons that include the bandwidthconstraint and power dissipation at the gateway, and bandwidthlimitations for the long-range link. Furthermore, there is a scalabilityissue associated with the arrival of data from many other clusters atthe remote monitoring site 2210.

These scalability issues are addressed in the WINS NG network usingextensive signal processing at the source, and by the management of theentire network as a distributed and active database. Additionally, theseissues are addressed by alternative network topologies, such asmulti-hop networks which avoid the bottleneck of the long-range link.Such networks permit multiple paths for propagation of data from asource to an end destination. However, in general, the scalabilityproblem cannot be solved purely at the level of the network; signalprocessing and data management issues must be addressed.

FIG. 23 is a bandwidth budget 2300 for a distributed sensor network ofan embodiment. Node-to-gateway communication links 2302 have a higherbit rate than the long range gateway-to-remote monitoring sitecommunication link 2304 due to the large power cost associated with thelong-range link. Local communication between nodes, exploiting thehigher bit rate at low power associated with the short range link 2302,permits local cooperation among nodes for the purposes of reaching alocal event detection and identification solution.

The bandwidth budget 2300 shows the sequence from sensor signal 2306through node 2308 (corresponding to a maximum rate of 12 kbps, followedby the low power air interface link to the gateway 2310). Finally, afteraggregation of data at a gateway 2310, long range, high power RF links2304 will carry summary messaging. Note that the aggregate sensor datarate is 12 kbps. This provides a significant margin below thenode-to-gateway communication link 2302 (with an air interface data rateof 100 kbps). This margin is not normally required since it is the goalof processing at the node 2308 to always reduce this data rate. However,while not advisable for optimal network operation, multiple sensors maysimultaneously be queried for testing, and higher rates may be needed onoccasion for local cooperative functions, such as beamforming.

The estimated node message bit rate corresponds to the bit rategenerated by a cluster of nodes exchanging low duty cycle status andconfiguration messaging. In an embodiment, the preprocessor to modeminterface data rates are lower than the air interface data rate.Applications support the filling of the RF modem transmit buffer over aperiod, followed by communication of the buffer over a short intervalavailable in an appropriate TDMA frame. Multiple user inputs are allowedat the gateway modem. FIG. 24 shows a WINS NG data frame format 2400 ofan embodiment. This data frame format 2400 supports both unicast andbroadcast.

The multicluster network is but one of many possible embodiments for theWINS NG network. The layering of software interfaces enables a varietyof link-layer protocols that can be used in combination with standardnetwork protocols such as Internet Protocol Version 4 (IPv4) or Version6 (IPv6). For example, the basic link layer protocol can have a flathierarchy, and use a wide variety of error correction methods, yet stillenable standard protocols to be used from the network layer up.Alternatively, specialized protocols could be employed which takeadvantage of the APIs that enable access to lower levels in the protocolstack.

Another issue related to scalability is the development of distributedself-organization and reconfiguration algorithms. As a network grows insize, it becomes difficult to centrally organize node connection anddeletion, synchronism, resource management, and issues related tonetwork maintenance. Thus, distributed protocols are required for largenetworks.

FIG. 25 is an example of self-configuration in a WINS NG network 2500 ofan embodiment. Network 2500 includes gateway node 2506, database 2512,and remote user interface 2510 connected to Internet 2508. Gateway node2506 is a gateway to the Internet 2508 for nodes 1-7. The network 2500is self-organizing with respect to the ability to distribute somecombination of information and energy, and in determining where and howthe processing and storage are to be accomplished. The network 2500programs and directs network resources to tasks according to prioritiesprovided to the network in response to the addition and deletion ofnetwork resources. The self-organization of the embodiment accounts forheterogeneity and inclusion of interaction with outside resources vianetworks like the Internet. The network 2500 is treated as a distributedand active database with the entire application built in a distributedfashion. Consequently, there are distributed algorithms for allapplication components.

The network 2500 is constructed using a distributed resource managementprotocol that operates on individual elements in order to assemble acomplete application. The network 2500 elements may be reused amongdifferent applications, or used in multiple classes of applications. Forexample, connections 2502A-2502J indicate participation in a first classof applications, while the connections 2504A-2504H indicateparticipation in a second class of applications. Both the first andsecond class applications are subject to reconfiguration when nodes areadded to or deleted from the network.

FIG. 26 is another example of self-configuration in a WINS NG network2600 of an embodiment. The network 2600 includes gateway node 2606,database 2612, and remote user interface 2610 coupled to the Internet2608. Gateway node 2606 is a gateway to the Internet 2608 for nodes 1-7.Construction of complicated combinations of network components isenabled from a small set of basic network components. In enabling thisconstruction, hardware and software are provided for connection ofheterogeneous devices that enable all devices in a family to form anetwork. In this embodiment, the software is designed to supportsoftware reconfiguration and upgrades. The protocols for the differentclasses of nodes may be embedded, but are not so limited.

The network 2600 includes a mixed wireless and wired network, whereinthe network 2600 is set up using a self-organizing protocol. Theprotocol recognizes the difference in power costs for various wired andwireless links and preferentially uses the links with lower power costswhen establishing communication routing through the network. Theprotocol may also take into account remaining energy reserves for nodeson the path, or the priority of the message being carried to determinerouting. The gateway nodes enable connections to networks not conformingto the core network hardware and software standards for connection ofheterogeneous devices.

Couplings 2602A-26021 represent wired connections and couplings2604A-2604F represent wireless connections. The same multiple accessprotocol and baseband modulation techniques are used for both the wiredand wireless connections, where the protocol used is one appropriate forthe wireless connections. This reduces cost for the communicationsdevices, and while not optimal for the wired connection, its cost (incommunication device and energy for usage) is typically far below thatof the wireless link. If the wired connection between sensor nodes 5 and7 is broken or disrupted, the network switches to using wirelesscommunications between nodes 5 and 7, with minimal network disruption.Alternatively, the communication between nodes 5 and 7 may be reroutedthrough node 6 in order to lower the power consumption associated withthe communication.

WINS NG multihop scalability relies upon information aggregation toavoid unbounded bandwidth growth. Specifically, information propagatesin one embodiment of a WINS NG network distribution as message packetsfrom information sources distributed within a network to informationsinks. As message packets approach an information sink, bandwidthrapidly scales upward. The WINS NG system incorporates node levelprotocols distributed and managed by the WINS NG database that aggregatemessages into compact forms, thus drastically reducing bandwidthrequirements. The assignment of message aggregation protocols occursalong with network assembly. Message aggregation protocols are adaptiveto node density, the environment, energy availability, and messageurgency. Any such protocol relies upon the APIs being able to make suchinformation available. Aggregation and distribution issues areintimately connected to the data processing and database managementprocedures in the WINS NG network.

The length of time a particular type of information is stored and howfar it is permitted to propagate and with what priority has largeimplications for resource usage. The WINS NG network protocols providefor management of these issues, as well as dealing with the likelihoodof heterogeneous resources in the network.

Information aggregation and data rate management in the WINS NG systembenefit from data predistribution. Code and data objects that areanticipated to be needed at a future time are propagated as low prioritymessages through the network. These objects may then be registered andcalled upon at a future time. Management of predistribution, via theWINS NG database, enhances system performance.

A WINS network of an embodiment can be regarded as a web of overlappingprocesses, linked through common resource usage priority sets, which areset by user queries. The APIs enable this network by providing networkresource information and message priority information to assetsthroughout the network. In this way both centralized and distributedresource management algorithms are enabled. In particular, directeddiffusion algorithms are enabled for synchronization, routing, andenergy management.

Consider a scenario in which there is a network gateway, and a largenumber of nodes, not necessarily all of the same type. If there is verylittle activity, and the application does not demand the gateway bealerted with low latency, then all nodes can be basically asynchronous.Indeed, even the boot-up need not establish tight synchronism. Routingtables back to the gateway can be established with packets beingtransmitted outwards, and moving on when a link pair comes up. Therouting tables record resource usage in transit. Information packetsthen flow downhill towards the gateways along minimum resource usepaths, according to the boot-up packets received by a given node.

Another network application demands frequent traffic to a group of nodesremote from the gateway. It would be inefficient from an energy point ofview for all nodes in the intervening space to have to go through timingacquisition for each packet transmitted, and so they proceed to maintaina tighter level of synchronism. This also reduces the transit time forthe messages flowing in each direction.

In yet another application, other nodes sporadically exchangeinformation with the gateway. Because the path to the high traffic areais synchronous, it represents a lower energy cost than using multipleasynchronous hops. This other traffic will now tend to get routedthrough this high traffic path, reinforcing its synchronism. This istrue even if latency is not part of the cost function for pathselection. It is enough that the synchronism level is energy usageaware, although path reinforcement is greater if low latency is also anissue.

Therefore, the APIs enable different components of the network tooperate at close to their minimum cost, with distributed algorithms, andrunning multiple applications over heterogeneous devices by publishingresource costs and message values. In this way the overall networkrequirements are not determined by the most demanding application, andperformance scales according to the resources available without centralplanning; hierarchies come and go as the application demands.

The multi-user, multi-channel, frequency-hopped WINS NG RF modem is usedin network self-assembly and reconfiguration. The WINS NG networksupports both self-assembly of randomly distributed nodes, with orwithout available GPS location information, and joining of new nodesinto the network. FIGS. 27 and 28 illustrate protocols for radios thatare establishing links in an embodiment. Communication between networknodes, and nodes within subnets, occurs according to assigned channelsdefined by pseudorandom frequency hopping patterns. In addition, thenatural requirements for low communication duty cycle mean that nodesare synchronized and operate in a time division multiple access (TDMA)frame structure in which the frequency hopped channels are active.

Self-assembly protocols are implemented for applications including largearea security applications and local area WINS tag asset managementtechnologies. These protocols are scalable and have been demonstratedfrom local area WINS NG networks to global satellite telephony link WINSNG nodes. Network self-assembly for a node distribution begins with theWINS NG nodes operating in search and acquisition modes in a search forparticipating peer neighbors and gateway nodes. Network self-assemblyoperates in a hierarchical messaging method where energy-efficient shortpackets are transmitted at low duty cycle and receiver operation is alsomanaged at low duty cycle. Upon detection of a neighbor, individual WINSNG nodes enter into a challenge and response session that escalates indata volume until it is established that a node has joined the networkor is not permitted to join. The network population is surveyed atrandom intervals for new nodes and missing nodes. Changes in nodepopulation or degree of link quality and RF signal strength are notedand communicated to the network gateways and server.

During steady-state operation of the WINS NG network, suddenlyoccurring, high priority events demand rapid response from the networkat a rate that may exceed normal message propagation time. The WINS NGsystem includes protocols that, when invoked by the receipt of prioritymessage codes (e.g. from IPv6 flow control labels or similar mechanisms)will initiate inhibition processes. Messages are broadcast to nodesadjacent to a path that will inhibit messaging from nodes other thanthose engaged in conveying the high priority event. In addition, theseprotocols act to reduce response time for messages following a preferredpath.

In the multi-cluster network of an embodiment, a distributed method forself-assembling the network accommodates the fact that many commerciallyavailable radios, such as most spread spectrum radios, cannotcommunicate to every other radio in range. Rather, one radio isdesignated as a master, or base, and all the rest are designated asslaves, or remotes. FIG. 29 shows a single radio cluster 2900. A radio,whether a base or a remote, can be generically referred to as a “node.”A base 2902 can communicate with all remotes 2904 within its range, buteach remote 2904 can only communicate with one base 2902. Thus each base2902 defines a strict cluster 2900, which is composed of all the remotes2904 in its range.

FIGS. 30 and 31 show multi-cluster networks 3000 and 3100. Because someradios 3004 belong to more than one cluster, it is possible for them torelay information between the two clusters 3000 and 3002. This can beaccomplished for example by the gateway periodically switching whichmaster it communicates with, queuing messages to be passed betweenclusters, until the appropriate connection is made. In other situations,the radios may allow contact to be made with multiple masters, in whichcase messages may be passed with greater ease between clusters. Withmany clusters 3100 it is possible for any of the radios in any clusterto communicate with any other radio in any cluster, as long as eachcluster has some overlap with another.

A multicluster-multihop network assembly algorithm should enablecommunication among every node in a distribution of nodes. In otherwords, the algorithm should ensure total connectivity, given a networkdistribution that will allow total connectivity. One such algorithm ofan embodiment is described below.

The algorithm runs on each node independently. Consequently, thealgorithm does not have global knowledge of network topology, only localknowledge of its immediate neighborhood. This makes it well suited to awide variety of applications in which the topology may be time-varying,and the number of nodes may be unknown. Initially, all nodes considerthemselves remotes on cluster zero. The assembly algorithm floods onepacket (called an assembly packet) throughout the network. As the packetis flooded, each node modifies it slightly to indicate what the nextnode should do. The assembly packet tells a node whether it is a base ora remote, and to what cluster it belongs. If a node has seen an assemblypacket before, it will ignore all further assembly packets.

The algorithm starts by selecting (manually or automatically) a startnode. For example, this could be the first node to wake up. This startnode becomes a base on cluster 1, and floods an assembly packet to allof its neighbors, telling them to be remotes on cluster 1. These remotesin turn tell all their neighbors to be bases on cluster 2. Only nodesthat have not seen an assembly packet before will respond to thisrequest, so nodes that already have decided what to be will not changetheir status. The packet continues on, oscillating back and forthbetween “become base/become remote”, and increasing the cluster numbereach time. Since the packet is flooded to all neighbors at every step,it will reach every node in the network. Because of the oscillatingnature of the “become base/become remote” instructions, no two baseswill be adjacent. The algorithm can also be stated as follows:

Algorithm Initial Conditions, for each node:

Acquired=false

Touched=false

Network=0

Assembly Packet Contents:

int Cluster

bool Acquired

Upon receipt of assembly packet:

if Touched==true

exit

Touched=true

if ReceivedAssembly.Acquired=true

Acquired=true

Send Assembly Packet to all Neighbors

NeighborAssembly.Cluster=ReceivedAssembly.Cluster+1

NeighborAssembly.Acquired=false

Become Remote on ReceivedAssembly.Cluster else

Send Assembly Packet to all Neighbors

NeighborAssembly.Cluster=ReceivedAssembly.Cluster+1

NeighborAssembly.Acquired=true

Become Base on ReceivedAssembly.Cluster

To Start the algorithm, choose any node in the network and have itbehave as if it received an assembly packet with

Cluster=1

Acquired=false.

This causes the node to send assembly packets to all its neighboringnodes, then become a base on cluster 1. It essentially becomes the firstbase in the network, and all of its neighbors become remotes in itscluster. The assembly packets spread out in all directions, and thenetwork assembles itself. The assembly packets essentially flood throughthe network as fast as possible. Because any traffic generated from anode could not possibly overtake the assembly packets, and because eachnode is ready to communicate after the receipt of an assembly packet,each node can assume normal operation after the receipt of an assemblypacket. Once a node has received an assembly packet, it is ready tobegin normal operations. Communication with other nodes in the networkis enabled with routing algorithms. The algorithm is self-terminating,by virtue of all nodes keeping track of whether they have been“touched”.

The assembly algorithm guarantees connectivity in a distribution ofnodes, if total connectivity is possible. FIGS. 32-34 show a networkself-assembly example of an embodiment. In the base case of two nodes3202 and 3204 that are within RF range of each other, the starting nodewill become a base 3202, and the other will become a remote 3204, asshown in FIG. 32. If a new node is added, assuming that it is within RFrange of at least one of the existing nodes, there are twopossibilities: (1) if the new node 3302 is within range of an existingbase 3202, it will become a remote 3302 on that base's network 3300, asdepicted in FIG. 33; (2) if the new node 3402 is not within range of anyexisting bases 3202, but only remote(s) 3204, it will become a base3402, as shown in FIG. 34. Nodes can continually be added, and they willalways fall into one of these two cases. Thus this algorithm scales upthe number of nodes indefinitely.

Several variations are possible using this algorithm. Many commerciallyavailable radios take a long time to switch between two masters. Thus,it is important to minimize the number of hops needed to communicate.One way to accomplish this is to have masters serve as the nodes withthe largest number of neighbors. This may, for example, be accomplishedusing a greedy algorithm in which nodes with more connections than somespecified fraction of their neighbors become masters. The clusterleadership can then be adjusted in one or more iterations. While notguaranteed to have the minimum number of hops for any routing throughthe network, it works better than random assignments, and operates in adistributed fashion. The optimization can be applied as masters aredesignated when the network is being built outward from the initialbase.

The basic algorithm establishes a multi-cluster network with allgateways between clusters, but self-assembly time is proportional withthe size of the network. Further, it includes only single hop clusters.Many generalizations are possible, however. If many nodes can begin thenetwork nucleation, all that is required to harmonize the clusters is amechanism that recognizes precedence (e.g., time of nucleation, size ofsubnetwork), so that conflicts in boundary clusters are resolved.Multiple-hop clusters can be enabled by means of establishing newclusters from nodes that are N hops distant from the master.

Having established a network in this fashion, the masters can beoptimized either based on number of neighbors, or other criteria such asminimum energy per neighbor communication. Thus, the basic algorithm isat the heart of a number of variations that lead to a scalablemulti-cluster network that establishes itself in time, and that isnearly independent of the number of nodes, with clusters arrangedaccording to any of a wide range of optimality criteria.

It should be noted that network synchronism is established at the sametime as the network connections, since the assembly packet(s) conveytiming information outwards from connected nodes. This is in contrast toprior art multi-cluster algorithms which typically assume synchronism isknown, as well as the number of nodes in the network. In an AWAIRSnetwork, self-assembly of the network is coincident with establishmentof synchronism, but a flat, or peer-to-peer network, rather than aclustered network results.

Network self-organization of an embodiment includes gateways andservers, an architecture that supports plug and play web-basedapplications. FIG. 35 is a block diagram of a WINS NG gateway 3500 of anembodiment. The WINS NG gateway 3500 operates as a network bridge usingtelephony interfaces, wireless services, and network standards includingfor example Ethernet, DeviceNet, and ControlNet. The WINS NG gateway3500 includes, but is not limited to, a network interface 3502, moduleinterface 3504 and RF modem 3506. It may alternatively support onlywired connections.

The WINS NG gateway functions include, but are not limited to: protocoltranslation and network management separating the low bit rate, powerconstrained WINS NG network from high speed internet services;management of queries and commands originating from Internet users (thisfunction, including buffering of commands and data, provides a remoteweb client with high speed access to the network without placing largeburdens on nodes or ruining their operating duty cycle); interface tomultiple long-range communication physical layers including Ethernet,long range packet radio, wireline telephony, wireless cellulartelephony, and satellite telephony.

The WINS gateway nodes communicate using protocols including HypertextMarkup Language (HTML)/Extensible Markup Language (XML). An embodimentuses the Inmarsat satellite telephony modem capability to access a WINSNG network at points on the earth. The WINS NG gateway also has theability to link the WINS NG network, optimized for low cost, shortrange, low power communication links, with long range wirelesscommunication links.

The WINS NG server is used in the architecture of large area WINSdeployments, but is not so limited. The WINS NG server supportsenterprise-class database applications that enable management of largeWINS NG node populations and migration of code and signal processingobjects and data to an entire node population. The WINS NG serversystems also support applications that exploit dense WINS NG nodedistribution. For example, in a condition based maintenance application,there is significant value in collecting all events and event historiesin a database to enable development of machine failure prognostics ordiagnostics. The WINS NG server also provides Web services to users thatwish to acquire data from remote nodes. For example, a global WINSapplication can connect remote WINS NG networks to the WINS NG servervia satellite telephony services. The WINS NG server provides data andnetwork management of the many autonomous WINS NG nodes, globallydistributed. A Web client may then query the WINS NG server for images,data, image and data history, and data relationships acquired at anypoint on the earth.

The WINS NG Web applications enable remote users to query the WINS NGnetwork for node operation and configuration and node sensor dataincluding images. In addition, the user may select and reconfigureindividual nodes for sensing, signal processing, communication, andnetworking parameters.

High Reliability Access to Remote Nodes and Nerworks

Sensor network islands have vastly increased value when connected tolarge networks such as the Internet. For example, consider asensor/actuator network designed for monitoring and/or control of heavymachinery. Through linkage to the Internet and an appropriate database,operators can monitor the present status of the machinery, call uprecords of past operations, and tune parameters so that machineryoperates more efficiently. The operators may be located anywhere in theworld. In the absence of such connectivity, costly site visits wouldneed to be made to deal with any problems that might arise.

FIG. 36 is a WINS NG network architecture 3600 of an embodiment havingreliable access to remote networks. Redundant pathways 3602-3604-3610and 3602-3606-3610 provide robust access in complex operatingenvironments. Multiple WINS NG gateways 3604 and 3606 support the WINSNG network 3600 and provide redundant access to conventional networkservices 3610 through wireless or wired high bit rate links. Remoteusers and user services 3620 are coupled to the WINS NG nodes 3602 usingat least one WINS NG gateway 3604 and 3606 and conventional networkservices 3610.

A WINS NG network of an embodiment is used for applications includingremote monitoring of high-value industrial equipment. In thisapplication, it is essential that prompt alerts be issued if any flaw isdetected. Thus, there is support for a plurality of servers that gainaccess to the database of network observations and software, so thatfailure in any single server will not compromise the ability of remoteusers to evaluate machine performance. In addition, a variety of networkphysical layers are supported, so that a variety of devices caninteroperate to build the sensing and control functions. This enablesthe use of independent long-range transmission means (e.g., satelliteand terrestrial wired connection). Likewise, the systems include supportfor a plurality of gateways, so that a single point of failure will notobstruct remote access.

One protocol to automatically choose among alternative communicationpaths uses a periodic heartbeat signal sent down each of the paths. Ifacknowledgement packets are not received within a specified interval, analternative path is chosen based on a cost junction that includes somecombination of path latency, capacity, and cost. This cost junction isdetermined by the application. Since the alternative paths may not beable to carry all the data, the protocol notifies the application of thereduced capacity so that only higher priority messages are carried.

Database Access

Due to resource limitations, sensor networks may not communicate all rawdata to a central location. Rather, taking into account constraints onstorage, communications bandwidth, processing capabilities, and energy,they meet the priorities of the end user for information. In thisrespect, sensor networks can be conceived of as a special type ofdatabase, wherein the data have life cycles that depend on their contentand sequencing in time. For the network as a database, as opposed toconventional databases, it is not possible to cleanly separate storage,processing, and communication procedures, but there are advantages inexploiting commonalties. For network communications data management, theprocess can be described as a flow through different states.

To illustrate, FIG. 37 is an example scenario of Internet access anddatabase management of an embodiment. The sensor nodes include sensing,processing, communications, and storage capabilities. The Internetcoupling enables access to more powerful and numerous computational andstorage resources than those present in the sensor node network.

In operation, sensor node 1 detects source 1. In response, sensor node 1engages in several layers of signal processing and storage decisionmaking before reporting results to the gateway node 3702. Sensor node 6detects source 2 and, in response, engages in several levels of signalprocessing and storage decision making before reporting results to thegateway node 3702. The remote user 3706 is alerted to both the source 1and source 2 events and, in response, may query the database forprevious events that were the same as or similar to the source 1 andsource 2 events. Furthermore, the remote user 3706 may make decisions onfuture actions in response to the source 1 and source 2 events.

The gateway node 3702 acts on instructions regarding event priority inreporting both events and, in response, requests further details fromsensor node 1. Upon receipt by the gateway node 3702, the details arepassed to the database 3708 which, in one embodiment, is a long-termdatabase. The remote user 3706 may use the results to generate newdetection algorithm parameters. When generated, the new detectionalgorithm parameters are broadcast to all sensor nodes of the network.

In one embodiment, the network integrates sensing, signal processing,database functions, and networking into one unified framework. Thisframework can be referenced to a state machine. While the sensor nodeshave internal instantiations of this state machine, the collectivenetwork is described by a larger state machine.

FIGS. 38A and 38B are a diagram of a process flow of a state machine ofan embodiment. At 3802, raw data is collected at a node and placed in ashort term queue in the node. At 3804, energy detection is performed,and if the energy of the data signal collected is below a predeterminedthreshold the record of the collected data is discarded at 3806. If theenergy is above the threshold, first level classification of the data isperformed at 3808. If the data is below another predetermined thresholdas tested at 3810, the data is stored for an interval to allow responsefrom neighboring nodes at 3812. If the data is above the threshold,fusion data is solicited from neighboring nodes and classification isperformed after the data is collected from them at 3814. If theresulting data is anomalous, as determined at 3816 using predeterminedcriteria, it may be stored longer term to allow for adaptation of theclassification system, or discarded at 3828. If the resulting data isnot anomalous, a message to the user is launched at 3818.

The message informs the user that the data, or result, is available. Inone embodiment, the message is transmitted in a compact representation,for example, as an entry in a codebook which associates theclassification decision to the target parameters. It is determined at3820 whether there is congestion in the network. If there is congestion,the message is aggregated with other messages to be resent later, queuedto be sent later, or dropped, depending upon the priority assigned tothe message. If there is no congestion in the network, the message isdelivered to the end user at 3822. The end user may request data queuedat the aggregation phase 3824, or raw data, or may not reply to themessage received. The retrieval of requested data creates the potentialfor data contention in the network that must be independently resolved,for example, by queuing. The retrieved information is archived and/orsubjected to advanced processing at 3826. Advanced processing may resultin the issuance of a command for new data processing priorities and/orprocedures in the network.

Queries from the end user establish priorities for record processing,communication, and storage. Established priorities are managed in adistributed fashion by the network, for reasons of scalability.Conditions such as hold times for data, probabilities that data willpass through a threshold, etc., define the parameters of a Markovprocess (state machine). The relevant conditions are user-selectablewithin the physical limits of the devices involved. In conventionaldatabases, it is common for data to have varying lifetimes in differentlevels of the database; in other words, data is routinely aged out. Inthe WINS network events occur with such frequency that conventional dataaging is not workable. The WINS network also differs from conventionaldatabases in that in the WINS network less internal communicationoccurs, resulting in forced decentralized decision making.

The state machine representation of an embodiment provides ways tomanage the networking and database functions, which advantageously usethe commonality of the two tasks for distributed sensor networks. Forexample, naming and storage conventions for networks and databasesrespectively can be managed according to similar principles, reducingconversions of data. A query to determine the physical state at somelocation is not concerned so much with addressing a particular node, asretrieving some data. There is a similar formal structure for routingand data processing, for example, decision trees. There is no arequirement for unique addresses. Rather, information retrieval takesplace with the use of attributes of the data, such as sensor type andlocation, which allows reduced representations of the address comparedto networks designed to deal with general traffic.

The processing is performed in such a way as to permit distributednetwork and database management. Queries establish database search,networking, processing, and storage parameters. For this to be managedin a distributed fashion, primary database management is at the nodes,in the form of the processing of the physical world inputs. Otherwise,the amount of data that could be generated would far exceed thepossibilities for scalable networking and storage. Thus, the processingstructure is largely dictated by the communications, networking, andstorage constraints. Further, a similar formal structure for routing anddata processing is exploited in the protocol design. Routing from nodesto gateways may be organized as a set of possibly overlapping trees.This admits hierarchical decision making, allowing further processing atmultiple steps as data and decisions progress from nodes to the gateway.Database retrieval is also often based on trees, since differentattributes can be used for branching decisions, reducing search time.Thus, naming based on the attributes of the data produced by a node isuseful both in constructing routes and in data retrieval systems.

One class of distributed algorithms that can be applied to the problemof dealing with queries about the physical world is directed diffusionalgorithms. These operate by using local activation and inhibitionsignals to spur actions by neighbors. For example, a gateway mayactivate its neighbors by launching a query requesting information aboutinstances of particular vibration modes. This query gets passed outwardstowards sensor nodes that can produce this kind of information, and mayreduce their inhibition in sending reports that may drain their energyreserves. If the query is general in that any qualified node may answer,a return signal may inhibit further propagation of the message outward.In this way, only the nearer nodes respond and the network as a whole isnot burdened with a large number of overlapping tasks to respond to.

However, the same structure also applies to the larger database of whichthe sensor nodes are only one part. A query may get only as far as theportion of the database residing as part of the wired network, if therequired information can be found. Further, a prior search of thedatabase may limit the number of nodes that are specifically queried togather information that is missing, but needed to answer the request.This saves energy for the remote network, and allows more queries to besimultaneously processed. Thus, the activation request gets modified asprocessing, communication, and data retrieval operations are carriedout. This would be very difficult in any system which did not treatthese functions as part of a unified whole.

The WINS NG database technology accommodates the distributed nature ofWINS NG measurements. Significant value is derived from determining therelationships using declarative query languages (DQLs). In addition, thecapability to deploy code and data objects to distributed nodes whiledetermining the concurrency of this data facilitates the scalability ofthe WINS NG network.

Conventional database systems, designed for centralized management ofalphanumeric data sets, are not suitable for use in the WINS NG network.For example, WINS NG network data is unstructured, and has multipleforms that are managed including data time series, images, code and dataobjects, and protocols. Also, the classic constraints used in queryoptimization for conventional systems are not suitable for WINS NG wherecomputation, memory, and communication resources are constrained.

Embodiments of the DQLs for sensor programming and information retrievalinclude small footprint standard query language (SQL) database systems.The SQL database systems are distributed on WINS NG nodes, operating aseither common or network gateway elements, at WINS NG servers, and onother devices that are permitted to join the network. Web-based accessusing the WINS NG architecture permits communication of sensor signals,images, events, and signal processing code. Web-based access furtherpermits data queries by node type, location, event, signal, priority,traffic level, and other parameters. The WINS NG system includes eventrecognition and identification algorithms operating on the WINS NGpreprocessor and processor. These are supported by an API that permitseither high level development with these signal processing components orlow level development at the preprocessor level.

Some characteristics of an embodiment of a DQL for sensor programmingand information retrieval include a small footprint relational databasemanagement system (RDBMS) of event data, signal processing libraries,node status, network status, and error conditions. The DQL includesdata-driven alerting methods for synchronization and ensuring networkconcurrency of database elements. The DQL further includes a signalsearch engine (SSE) for indexing and information labeling ofunstructured sensor data sets. User data services include a WINS WebAssistant for access to remote and global RDBMS. Query processingoptimization systems are included for the WINS NG network. The RDBMSmonitors network status, maintenance, and security.

To rapidly design such combined sensor networks/databases, it isimportant to make use of standards wherever possible. The layeredarchitecture of the embodiment of WINS NG nodes discussed earlier makespossible use of both standard hardware components and standard protocolswhere appropriate to the application. For example, consider anindustrial monitoring application, where vibrations in large pumps areto be monitored for abnormalities. Records of behavior can be usefulboth in predicting failures (prognostics) or in determining causes afterthe fact (diagnostics) to reduce repair time and expense, and to assistin new design. Here the WINS NG nodes establish a network, eventually toa gateway, which then connects to a standard database server such as anSQL server, either locally or through the Internet. In either case, thedata generated by the sensor can be viewed either remotely or on-siteusing standard browser software. Further, the parameters for control ofwhat data is transmitted and eventually archived to the database canalso be set by user queries either locally or remotely.

This combination of a self-assembling sensor network, databaseconnection, and remote control via the Internet is an extremely powerfulmeans of monitoring and controlling remote sites. Experts are notrequired to be on site either to establish the network, or tosubsequently review or control how the data is collected, resulting inlarge efficiencies. A custom design of all these components would beprohibitively expensive in engineering design time, as would the ongoingcost of supporting the software; an integrated approach to the overallarchitecture, making use of standard components and software wheresuitable, leads to both rapid and highly functional design.

In one embodiment, the WINS NG database includes data-driven alertingmethods that recognize conditions on user-defined data relationships.For example, triggers are set for coincidences in arrival of signals,for node power status, or for network communication status. The WINS NGdatabase supports event data, signal processing libraries, node status,network status, and error conditions.

The WINS NG database addresses query optimization in the context ofenergy constraints in network communication. Thus, queries are managedsuch that proper decisions are taken regarding the need to communicatewith distant nodes. Query optimization also supports node-to-nodequeries where a node may improve the quality of a decision bycooperative methods, comparing results with the stored data inneighboring nodes.

Reconfigurability is an important characteristic of a WINS NG networkbecause nodes can remain in environments for the lifetime of a largesystem after installation. Network capabilities are reconfigurable asadvances are made in the ability to detect events. Specifically,database services are provided for marshalling code and data objects toremote WINS NG nodes to enable reconfiguration. For example, the WINS NGdatabase system provides services for distribution of large binaryobjects containing library elements to be linked at runtime, e.g.,dynamic-link libraries (DLLs). In addition, protocols and signalprocessing methods are remotely and globally reconfigurable.

Operational protocols, signal processing protocols, and networkprotocols are migrated via verified atomic transaction methods to ensurethat entire protocol sets are migrated completely and without error toglobally distributed WINS NG nodes. Particular protocols detectconcurrency errors. The WINS NG database includes data services forauditing concurrency of all data types. Also, a rollback protocol isprovided for execution in the event that concurrency errors aredetected. This ensures that node connectivity and operation is robustduring system upgrade phases.

In one embodiment, the WINS NG database is implemented in small footprint databases at the node level and in SQL systems at the WINS NGserver level. Remote SQL queries received at the server level accessboth the low level network and the server databases. Replication ofdatabase elements occurs between WINS NG nodes, between WINS NG nodesand WINS gateway nodes, and between the WINS NG network and servers orremote users.

Data services are also provided to both remote and local users.Specifically, remote users may interact directly with the WINS NGgateway devices, or directly with the WINS NG server. The remote usersare provided with a WINS web assistant for interaction with the WINS NGdatabase systems and with individual nodes and gateways for manyapplications. The WINS web assistant continuously publishes WINS NGnetwork results according to query scripts.

For example, consider the monitoring of large industrial pumps. WINS NGnodes can be embedded within the casings of the pumps, which aretypically in operation for many years. Other nodes can be on theexterior, and thus may be physically upgraded over time. It is desiredto monitor the vibrations or other physical characteristics of the pumpsto predict failure or verify continued proper operation. A database ofobservations over time from many pumps are used to develop betterdiagnostic and prognostic algorithms, which are downloadable to theremote nodes, whether embedded in the machinery or not.

A further example of the use of database methods of an embodiment isfound in the effective incorporation of both active and passive tagsinto systems which detect, track, and identify objects like security,logistics, and inventory systems. Passive tags in particular can be verylow cost, and thus very numerous. In a security application, an area canbe flooded with low cost tags so that there is a high probability thatat least one tag will adhere to any object passing through the region.In conventional usage of tags, a tag with a unique identifier isattached to a known object, achieving the binding between tag and objectin the database. However, when a sensor network is present this manualbinding is not necessary, since the network may identify the object.Typically, it is difficult to keep track of an object as it movesthrough different regions of coverage, and to know whether it is thesame object.

A tagged object by contrast is always distinct because of the uniqueidentifier of the tag. Thus, by linking the identifier of the tag withthe sensor measurements in the database, tracking is made easier.Moreover, by having easier access to the history of observations of theobject, it is more easily identified. Further, behavior of groups oftagged objects are correlated to location histories, to gain furtherinformation about their interactions, simplify the problem of dealingwith multiple targets in view, and provide a simpler means to name andthus archive data about objects. With active tags including, forexample, some combination of communication, storage, and sensingdevices, the tag itself can be part of the database, and provideextended detection range, by alerting nearby sensors via its radio to goto higher levels of alertness or to stand down and conserve powerbecause the object has already been identified.

Security Methods

The WINS system of an embodiment uses communication methods that providesecurity in communication for the short message packets that arefundamental to the WINS network. The communication methods also protectthe network from an adversary that attempts to join the network byposing as a participating WINS node, and from an adversary that attemptsto observe message traffic or the progress of challenge-responsesequences and thereby gain information. The network is also protectedfrom an adversary that attempts to derive network operating modes ornetwork threat detection capability by a simple traffic analysis basedon measurement of RF communication energy. In addition, the node andnetwork information is protected from a security breach resulting froman attempt by an adversary to recover a node and recover information. Itis important to provide these capabilities with the additionalconstraint that encrypted communication links must be robust against biterrors.

The challenges associated with achieving these goals are raised by theconstraint that WINS network message packets are short. In addition,energy constraints limit the available number of packets. Balancingthese challenges is the characteristic that typical security systemlatency is large. Specifically, long periods fall between successiveevents. This provides periods for background computational tasksassociated with encryption. The WINS NG platform partitioning enablesthe WINS preprocessor to manage WINS network and sensing functions whilethe WINS processor operates in the background.

In one embodiment, the WINS NG network implements security based onpublic key methods. Public key methods offer the advantages of beingscalable because few encryption keys need to be managed. This is aparticular advantage in the WINS network where the number of nodes isvariable and may increase suddenly as new nodes join a network. Inaddition, the public key method is reconfigurable and operates in ahierarchical fashion with a scalable key length.

Primary attacks on the public key method, such as brute force,exhaustive decoding, and impersonation are addressed for the distributedsensor application. First, message channels carrying short messagepackets may be attacked by brute force methods. In this case, anadversary first intercepts an encrypted packet. Then, by operating onall possible data packets using the public encryption method a match canfinally be made between these encrypted packets and the interceptedpacket. At this point, by this exhaustive search, the adversary hasrecovered the input code word. This can be continued for all packets.Protection against this exhaustive search is provided by or salting theshort packet with a random confounder code word. Then, if an adversaryrecovers the packet, only the data convolved with the random code wordis recovered. For WINS NG the confounder code word can be updated as arolling code for each message transmission.

For this method, and many other security functions, keys, signatures,and encrypted algorithms are communicated containing confounder codesequences. The algorithm for communication between nodes is transmittedto nodes using public key and digital signature methods in long packets.Here the WINS system takes advantage of the fact that the distributionof confounder codes and other security updates is infrequent, andtherefore is completed with secure, long message sequences.

While encryption methods involving confounder codes are robust inwireline communication systems, the WINS system accommodates theinevitable bit errors and packet loss that require retransmission.Simple confounder code methods that update for each transmittedcommunication packet would introduce a weakness. Here, loss of onepacket would break the sequence and disrupt communication, forcing thesystem to undergo a complete reacquisition and authentication cycle.Thus, WINS NG of an embodiment implements sequence management forconfounder codes such that as a result of an error, confounder codespreviously used may be reused to reacquire the code sequence. Thealgorithm for update of confounder codes may also be distributed usingconventional, secure, long packet, digitally signed messages. As notedabove, energy cost for this step is not significant because this stepoccurs infrequently.

The security attacks associated with message interception and anadversary impersonation attack are addressed by development of thepublic key and digital signature methods described herein. In addition,it is important to protect the network from an adversary who attempts togain knowledge about the network by observing the response to achallenge. This is addressed in WINS NG by development of zero-knowledgeprotocols. Here the network response to a challenge, for example amessage from an unknown node or user who attempts to join the network,must not be regular and predictable. Instead, network response carries arandom aspect such that the challenger, or possible adversary, cannotgain knowledge regarding the network by repeatedly requesting to join.

Network communication privacy is also protected. If RF communicationoccurs in direct and regular response to events, an adversary would bein a position to derive information regarding the ability of the networkto respond to events. This would enable an adversary to determine thesensitivity of particular sensor nodes or reveal other traits. The WINSNG system addresses this attack by implementing communication privacyprotocols. Specifically, communication of events and status informationis not keyed in time directly to events and is not periodic. Instead, arandom sequence of packet transmissions, a fraction being decoy messagepackets, is transmitted. Desired information is impressed on this“carrier” of random packets. An adversary receiving RF energy wouldreceive random “chaff” and would not be able to rapidly correlate eventswith transmissions.

Regarding the error protection functionality in a multihop WINS network,raw data for a packet is first encrypted, in a code known both to thesending node and the eventual end destination. Error control is alsoincluded. In one embodiment there may be only one packet exchangedbetween these nodes, the encryption and error control codes must beshort, and cannot extend past the packet boundaries. However, there canbe numerous exchanges of information between the origin node and itsimmediate neighbors, and these transmissions may be vulnerable toeavesdropping and jamming. Thus, the confounder sequence operates on thesequence of these packet transmissions, zero-knowledge techniques suchas addition of chaff packets are employed, and the sequence is protectedby error control codes.

These techniques do not operate end-to-end, but over individual multihoplinks to avoid both the requirements for sending long keys over multiplehops, and to reduce latency for retransmission. Each node in themulti-hop connection checks for errors, and demands retransmissions,using the next element of the confounder sequence if uncorrectableerrors are detected. Upon receipt of the error free packet, it removesthe chaff, undoes the confounding, and then applies the appropriateconfounder and chaff sequences for the next hop in the link. In thecontext of standard Internet layering, encryption operates at theapplication layer, where other techniques operate at the link layer. Intypical systems, all security operates at or near the application layer,but for WINS networks the traffic characteristics and eavesdroppingtechniques demand pushing some of these functions to lower layers.

Communication between the WINS NG node modules, in the event that thesemodules may be in separate packages, is also protected. Upon bootup orconnection between modules, authentication is completed. In this event,since the communication is by a direct wireline connection, the WINS NGcan complete this authentication via long public key methods.

Measures of encryption effectiveness for the WINS network measure theprobability of compromise relative to computational energy and timerequirements. Adversarial efforts to compromise the network requirecomplex and lengthy observation and computation that isorders-of-magnitude more difficult in computation and communicationresources than required by WINS NG. An additional barrier forcommunication privacy and security is maintained by multihop networkpower management where RF path loss is exploited to naturally veilcommunication channels.

Proper power control prevents adversaries from participating unless theyare in the midst of the network. Adversaries wishing to breech thesecurity barrier then must operate with extreme computational capabilityin relation to allied nodes and must be in the midst of the network atthe same density as WINS NG, an enormous expense for an opponent. TheWINS NG system also exploits the WINS Database systems for aperiodicsweeps of deeply embedded security information for the purpose ofrevealing any adversary or compromised node. Security sweeps can includestudy of the type of sensor data a node is expected to produce. If notconsistent with its claims, the node can be excluded, just as would bethe case for a failed node. Aperiodic re-authentication and remotesecurity monitoring are also provided. Low power implementation of thesecurity algorithms exploits partitioning of security functions betweenthe WINS preprocessor and processor.

Distributed Position Location

An important requirement in many sensor network applications is fornodes to be aware of their position. For example, in securityapplications if the nodes know their own location, then it is possibleto estimate the locations of targets. One way to enable this is to equipevery node with a position location device such as GPS, or to manuallyinform nodes of their positions. However, for cost reasons this may notalways be possible or desirable. An alternative is for a subset of thenodes to know their own positions, and then to distribute locationknowledge by using the communications and sensing means of the nodes. Itis assumed is the discussion herein that the nodes all have radiocommunications, and acoustic transducers and sensors. Synchronism isestablished using the radios, while ranging is accomplished using theacoustic devices. Both simplified and more accurate position locationmethods are described.

Acoustics have an advantage over RF for ranging because of the tolerancefor imprecise clocks. A timing uncertainty of 30 microseconds will at anominal propagation velocity of 330 meters per second (m/s) yield anaccuracy of 1 centimeter (cm). This requires a bandwidth of 33 kilohertz(kHz), which is easily achievable using ultrasound. In contrast, radioranging deals with velocity (v)=3×10⁸ m/s, and so six orders ofmagnitude increased bandwidth and timing precision are required if timeof arrival is used, while signal strength is a very poor indicator ofrange. Since position x=vt, where v is the a priori unknown propagationvelocity and t represents time, some reference locations are required todetermine the spacing between nodes. These will also serve to enabledetermination of absolute rather than relative positions. Oncedetermined, the velocity information can be supplied to all other nodesin the network.

If the two nodes involved in a ranging exercise know the nominalvelocity, and are synchronized, they can both determine their ranges andcompensate for wind. Node 1 launches a chirp or spread spectrum signalat a known time, and node 2 measures the propagation delay t1. Node 2then launches a signal at a known time, and node 1 measures the delayt2. The distance x=v1t1=v2t2. But v1=v+w and v2=v−w, where w is the windcomponent in the direction from node 1 to node 2. Thus there are twoequations in the unknowns of x and w. With three nodes, one can learnanother component of w and thus the ground velocity of the wind.

Without synchronization, two nodes cannot fully compensate for wind. Thebest that can be done is to launch a signal, and then send a returnsignal after some fixed delay. The round trip estimate ist1+t2=x/(v+w)+x/(v−w). For v=330 m/s, x=10 m, and w=10 m/s, a positionerror of 1 cm results. Since the network must establish synchronism forthe radios in any case, and needs it to estimate the velocity ofpropagation, there is no point in paying this small performance penalty.

Radio frequency signals will have negligible multipath delay spread (fortiming purposes) over short distances. However, an acoustic signal in a10 meter room could reasonably experience delay spreads on the order ofthe room dimension divided by the velocity, or 0.03 s. The multipath isresolved so as to use the first arrival as the range estimate. If thereis no line of sight and all arrivals are due to multipath, then theranging will only be approximate. If a time-hopped (impulse) or directsequence spread spectrum method is used, then the means for resolvingthe multipath is a RAKE receiver. There do not need to be enough RAKEfingers to span the entire delay spread (although the more the better interms of robustness of acoustic communications). The time spacing of thetaps is determined by the required position resolution and thusbandwidth of the ranging waveform. Thus, if 1 cm accuracy is desired,the tap spacing has to be 30 microseconds.

Note that the multipath characteristics are highly frequency dependent.The range of wavelengths for frequencies going from 1 kHz to 10 kHz are0.33 m to 0.03 m. Since scattering/transmission/reflection depend inpart on the ratio of the wavelength to object size, very differentresults can be expected at the different frequencies.

In an example of a network-building procedure of an embodiment, asituation arises wherein every node has learned the distances to itsneighbors, and some small fraction of the nodes of the network knowtheir true locations. As part of the network-building procedure,imprecise estimates of the locations of the nodes that lie within ornear the convex hull of the nodes with known position can be quicklygenerated. To start, the shortest distance (multihop) paths aredetermined between each reference node. All nodes on this path areassigned a location that is the simple linear average of the tworeference locations, as if the path were a straight line. A node whichlies on the intersection of two such paths is assigned the average ofthe two indicated locations. All nodes that have been assigned locationsnow serve as references. The shortest paths among these new referencenodes are computed, assigning locations to all intermediate nodes asbefore, and continuing these iterations until no further nodes getassigned locations. This will not assign initial position estimates toall nodes. The remainder can be assigned locations based on pairwiseaverages of distances to the nearest four original reference nodes. Someconsistency checks on location can be made using trigonometry and onefurther reference node (say, the apparently closest new one), todetermine whether or not the node likely lies within the convex hull ofthe original four reference nodes.

In two dimensions, if two nodes have known locations, and the distancesto a third node are known from the two nodes, then trigonometry can beused to precisely determine the location of the third node. Distancesfrom another node can resolve any ambiguity. Similarly, simple geometryproduces precise calculations in three dimensions given four referencenodes. But since the references may also have uncertainty, analternative procedure is to perform a series of iterations wheresuccessive trigonometric calculations result only in a delta of movementin the position of the node. This process can determine locations ofnodes outside the convex hull of the reference nodes. It is alsoamenable to averaging over the positions of all neighbors, since therewill often be more neighbors than are strictly required to determinelocation. This will reduce the effects of distance measurement errors.

Alternatively, the network can solve the complete set of equations ofintersections of hyperbola as a least squares optimization problem. Thisis undesirable for many reasons, not least because it is not easilytransformed into a distributed computation, and due to its potential tobe highly ill-conditioned. A decentralized calculation such as the oneoutlined above can converge fairly quickly with only local informationexchanges being required after the initial position guesses have beenmade. It can stop whenever the change in position from a previousiteration is small enough.

Position accuracy on the order of one centimeter might be needed forpurposes such as coherent acoustic or seismic beamforming. But for mostpractical purposes much reduced accuracies suffice. For example, ifnodes are relatively closely spaced, it may be sufficient to track atarget as likely being confined by the convex hull of several nodes.Then accuracies on the order of meters may be good enough. The quickmethod with one or two trigonometric iterations might suffice in suchcases, and it would require much simpler acoustic transducers andreceivers.

FIG. 39 is an example scenario of network element self-location in asensor network 3900 of an embodiment. In this example, sensor nodes 2,5, 8, and 9 contain an absolute position and timing reference mechanism,such as GPS. Furthermore, any or all of the sensor nodes may includetransducers for acoustic, infrared (IR), and radio frequency (RF)ranging. Therefore, the nodes have heterogeneous capabilities forranging. The heterogeneous capabilities further include differentmargins of ranging error and means to mitigate ranging variability dueto environmental factors such as wind. Furthermore, the ranging systemis re-used for sensing and communication functions. For example,wideband acoustic functionality is available for use in communicating,bistatic sensing, and ranging.

The advantages of heterogeneous capability of the nodes are numerous,and are exemplified by use of the ranging functionality in providingcommunications functions. As one example, repeated use of thecommunications function improves position determination accuracy overtime. Also, when the ranging and the timing are conducted together, theycan be integrated in a self-organization protocol in order to reduceenergy consumption. Moreover, information from several ranging sourcesis capable of being fused to provide improved accuracy and resistance toenvironmental variability. Each ranging means is exploited as acommunication means, thereby providing improved robustness in thepresence of noise and interference.

Applications of Wins NG Technology

There is a wide range of applications of the WINS NG technology having avariety of sensing, processing, and networking requirements, all ofwhich can be met with embodiments of the WINS NG technology. Theseapplications include, but are not limited to, PicoWINS, hybrid WINSnetworks, dense security networks, asset tracking andmanagement/manufacturing, wireless local area networks (LANs), wirelessmetropolitan area networks (MANs), composite system design and test, andvehicle internetworking.

PicoWINS

Between WINS NG technology on the one hand and passive radio frequencyidentification (RFID) tags on the other, there are a range ofapplications that require some combination of sensing, signalprocessing, and networking in compact and low-cost systems, with reducedcapabilities compared to WINS NG but increased capabilities relative toRFID tags. The PicoWINS embodiment employs many features of the WINS NGtechnology, but integrates them into more compact, low-power devices.This enables deployment in much greater numbers than WINS NG.

The provision of two-way communications to remote devices enables hybridnetworks in which not every element must have the same capabilities. Inparticular, gateway nodes (e.g., WINS NG nodes) can include the abilityto connect to external networks, user interfaces, mass storage, andpowerful processing. Thus, the behavior of the remote nodes can becontrolled by the gateway (or the network beyond the gateway) so thatthe remote nodes have a high level of functionality without needing allthe features of the gateway node. Two-way communication further enablesmultihop networks, expanding the coverage range for each gateway. Thislowers the total cost of providing coverage of a particular region.

One embodiment of PicoWINS employs flexible, thin film substratepackages, new communication and networking strategies, and new sensingmethods. Nodes of this embodiment of PicoWINS are conformal and may beembedded in many packages, marking a departure from previoustechnologies. Such nodes in various embodiments attach to boots andvehicle tires and treads, and detect proximity, touch, sound, and light.The nodes incorporate new microelectronics for low power, and exploitnew methods developed for PicoWINS that provide cooperative sensing andcommunication in a power constrained and low cost system. In oneembodiment, communication physical layers include both RF and acousticmethods. Also, PicoWINS carry processing systems adapted to security.Finally, PicoWINS is interoperable with large-scale WINS networks (e.g.,WINS NG) and links via redundant gateways to standard network services.PicoWINS is directed to the most ubiquitous tier of the WINS hierarchy,namely, low-cost, thin, conformal, micropower, autonomous devices.

FIG. 40 is a PicoWINS node architecture 4000 of an embodiment. ThePicoWINS node 4000 includes a micropower sensor 4002, communicationpreprocessor 4004, and accompanying processor 4006. Sensor interfaces4008 are included along with RF and acoustic power management 4010. Thedevice may also optionally include interfaces for wired communications.This PicoWINS architecture compares to WINS NG in the absence ofhigh-level processors and their associated interfaces and peripherals.Thus, PicoWINS may be more compact.

PicoWINS nodes employs APIs that mirror the functions of WINS NG nodesup to the level of the interface between the preprocessor and processor.That is, while different physical components are employed, applicationsrunning on WINS NG nodes do not require special modifications to dealwith a network that includes PicoWINS nodes. Using the APIs, thefunctions of the PicoWINS nodes are embedded, without the need forkeeping commonality of electronic components. Further, PicoWINS networksimmediately gain access to the many resources available to WINS NGnetworks, by means of connections to WINS NG nodes and gateways. Thus,hybrid combinations are enabled wherein, for example, signal processingtasks are split between the different classes of nodes, in a mannersimilar to the splitting of tasks between processors and preprocessorswithin WINS NG nodes.

FIG. 41 is a hybrid network 4100 including PicoWINS nodes 4102 of anembodiment. The PicoWINS nodes 4102 are scattered in the environment,for example, by manual emplacement, air drop, or delivery of a munitionscanister filled with PicoWINS packages. The PicoWINS nodes 4102 detectmotion and presence. In addition, if a PicoWINS node 4104 is attached topassing personnel and/or vehicles, its motion and ultimate departure isnoted by the network and communicated to the network. The PicoWINS nodes4102 communicate status, network management, and sensor eventinformation, but are not so limited. Through the WINS NG gateway 4106,access to a wide area network 4108 such as the Internet, and theassociated services as described with reference to WINS NG networks areavailable. For example, the PicoWINS devices support programmability byremote web clients, and the WINS gateway provides access to a databasefor querying PicoWINS status and events.

A frontier in global network extension is the connectivity of theInternet to deeply distributed processors, sensors, and controls. ThePicoWINS system provides low cost devices that are deeply and widelydistributed in environments and integrated into equipment to providecontinuous, global sensing and monitoring of an area, area and facilitysecurity, environmental status and sensing, and monitoring of globallydistributed assets. In contrast to the restrictions and cost ofconventional wireline networking, the low power wireless networkingbetween PicoWINS nodes provides the deep and dense deployment requiredfor these applications.

A state machine architecture allows sensing, signal processing,computation, communication, and power management, supported in a robustand convenient coding method. The PicoWINS state machine of anembodiment enables low power operation, contains all of the PicoWINSfunctions and variable timing and response requirements in one set oflinked modules, and has the ability to rapidly develop and reconfigure.It is implemented using a PicoWINS board that includes an analog sensorinterface, but is not so limited.

A state machine controls sensing, signal processing, event recognition,communication, and power management, but is not so limited. In addition,the state machine manages network assembly by controlling search andacquire messaging by nodes. The state machine is implemented to allowfor direct access to analog sensor inputs. Typical implementations ofnode-level protocols have involved algorithms and code that are fixed innature, making changes or optimization difficult. By contrast, thePicoWINS state machine manages the myriad node events in an organizedfashion that is convenient and transparent to the developer. In oneplatform, the node functions of sensing and communication, previouslydifficult to integrate in a compact processor, are managed easily.Separate adjustment of communication, sensing, and decision functions isenabled without resorting to large code changes. This state machinearchitecture also lends a particular degree of convenience todevelopment for micropower processors for which only limited codedevelopment support is available.

A critical challenge for tactical sensor nodes is the inherent reductionin sensor sensitivity that accompanies scale reduction. In the case ofconventional methods, as the scale of the node is reduced, there isdegradation in sensor performance. By contrast, an embodiment of thePicoWINS system exploits the package as a sensor. By employing theentire package, some of the limitations of compact geometry areeliminated. In addition, by selecting a piezoelectric package system,sensing operations may be performed without power dissipation on thepart of the sensor. In one embodiment, the package design implementsseismic vibration detection with the required sensing proof masses beingformed by battery cells. The package itself has appropriate scale topermit low frequency vibration measurement.

The PicoWINS design also provides nodes carrying a mix of sensorcapabilities. For example, while all nodes may carry seismic and opticalsensing, some nodes may carry magnetic, or other sensor systems. Acombination of sensors can be selected for a specific deploymentenvironment. One of the primary challenges for implementation of compactsensor nodes is providing the required continuous sensing andcommunication availability in a compact package without the use ofconventional battery cells. An innovation for PicoWINS is thedevelopment of a tag that requires only sensors that do not require acontinuous bias current or voltage.

FIG. 42 is a PicoWINS node 4200 of an embodiment. The PicoWINS node 4200is packaged using thin film, flexible systems 4202, but is not solimited. The substrate 4202, in this case the piezoelectric polymerPVF₂, operates as an acoustic sensor and source. Thin film, flexiblephotovoltaic devices 4204 are also incorporated into the substrate 4202to provide an energy source and an optical presence detection sensor.Furthermore, an antenna 4208 can be incorporated into the substrate orcarried on the substrate. A micropower complementary metal-oxidesemiconductor (CMOS) application specific integrated circuit (ASIC) die4206, or alternatively, a small board or multichip module occupies thecenter of this structure. The PicoWINS node 4200, or sensing element,exploits micropower CMOS technology including a low light visiblephotodetector channel, CMOS passive IR sensor (polysilicon bolometer),and PVF₂ vibration and acoustic sensor. The PicoWINS sensors reducepower dissipation with piezoelectric and optical sensors that require novoltage or current bias.

Flexible substrate piezoelectrics offer many advantages as sensors forthis application, providing signal outputs greater than 10 Volts for thelarge deflections associated with the motion of a PicoWINS node that isattached to moving threat vehicles or personnel. This is accomplishedwithout power dissipation. Several piezoelectric films have beendeveloped into highly responsive touch/pressure sensors that areembedded directly into the sensor substrate. Specifically, Kynar, abrand name for polyvinylidenedifloride, (PVF₂) a polymer film, can bepolarized and thus made to possess many piezoelectric and pyroelectricproperties. The piezo effects can be initiated by substrate compressionor longitudinal stretching. By laminating the thin Kynar onto a flexiblesubstrate, bending causes an off axis moment which causes a longitudinalstretching thus producing a proportionally large output voltage. Varioustypes of piezo sensors may be used in this application, ranging frominsulated coaxial cable pressure sensor structures to thin 40 micrometer(μm) sheets of raw Kynar. The piezo effect is much more pronounced inthe sensor where the Kynar is laminated to a support material such asKapton or Mylar.

Additionally, embodiments for an accelerometer having moderatesensitivity have been developed using the same Kynar/Kapton substrate asthe sensor. In this embodiment, proof masses are suspended in adistributed arrangement around an interface system. Compact batterycells then serve both as a power source and as the proof masses. Thesubstrate is used as the suspension. A relatively low resonant frequencyis designed allowing for increased sensor response.

Other applications of the substrate include photoelectric cells andpyroelectric phenomena for use as a back-up power source as well as amotion detector. The photoelectric cells can absorb enough energy in oneday for several days of operation, when combined with the efficientpower management techniques in place, and the passive sensors. In oneembodiment, the substrate can be designed as an integral structuralelement. In one embodiment, a maple-seed shape is adopted so that thenodes may be deployed by air, and fall in a stable-rotating pattern. Thesubstrate thus not only provides sensing capability, but aerodynamicbenefits. In another embodiment, the substrate flexibility permits thecreation of sensor “tape”, that can be unrolled to different lengths(e.g., for a perimeter) as required.

A PicoWINS node of an alternate embodiment uses a high dielectricsubstrate to minimize antenna size in conjunction with limited localprocessing, networking, and sensing capability. These nodes are ofminimal volume and cross sectional area so as to reduce cost, facilitatedelivery, and create an unobtrusive sensing capability. The alternatePicoWINS nodes have numerous integrated capabilities including, but notlimited to: sensor processing; RF front end and IF band for low bit ratesecure message communication; accelerometer, inductive, acoustic, andproximity sensors; RF ranging capability leveraging communication withother node types including resource rich WINS NG nodes; local battery orpower supply; multiple power modes of operation for extended lifetimes;resonant annular ring patch antenna for efficient transmit and receivepower use; and, adhesives to enable attachment to a passing object.

FIG. 43 is a block diagram of a PicoWINS node 4300 of an alternateembodiment. This PicoWINS node 4300 provides a compact sealed structure.The electronics are nested within the center of the annular ring patchantenna away from the high field set up between the patch antenna 4302and the ground plane 4304, thereby minimizing the size. Enclosed in thecenter of the ring are at least one battery 4306, processor 4308, andintegrated sensors 4310. The node 4300 provides high input impedance tothe antenna 4302 associated with the lowest order mode of the annularring patch antenna, with the antenna numerically simulated withcommercial full-wave software for optimal operation with the enclosedbattery, electronics, and high dielectric substrate. An adhesivemechanism or layer 4312 is coupled to the top and bottom of the antenna.The adhesive mechanism 4312 includes plastic hooks, burrs, Velcro, glue,and electromagnets, but is not so limited.

The PicoWINS nodes interoperate to enhance the capability andflexibility of the entire network. In addition to providing low-costlimited functional networking to WINS NG nodes, a variety of PicoWINSnodes operate in the same environment leveraging the capabilities ofeach system option. An embodiment of PicoWINS network operation includescommunication among separate thin film piezoelectric substrate PicoWINSnodes and lower cross-sectional area annular ring antenna PicoWINSnodes, networking each with a distinct sensing and distinct rangingaccuracy. Providing modular processing hardware, common communicationprotocols, and APIs enables the low cost creation of a variety ofPicoWINS systems, each optimized for particular sensor capability,ranging accuracy, size or level of unobtrusiveness, and operatinglifetime.

In addition to modularity for limited size and power requirements,embodiments of PicoWINS nodes can take advantage of dual use ofhardware. As an example, acoustic sensors can be integrated with anacoustic ranging front end. Furthermore, antenna input can be monitoredat transmission (reflection coefficient) to detect motion throughchanges in the antennas near field environment at a single frequency(providing an electromagnetic sensor which may be enhanced at cost tocommunication efficiency by using an antenna sensitive to detuning suchas a sub resonant ring or dipole).

In the area of antennas, electromagnetics for compact tactical tags andwireless sensors have not previously been optimized for an applicationlike PicoWINS. First, the PicoWINS tactical tag system places uniquerequirements on the size of wireless communicators. Active wirelessdevices have previously been excessively large for tactical tagapplications. Radio frequency identification (RFID) tags are availablein compact package form; however, these devices require high powerinterrogators immediately adjacent (typically within 1 m) to the tag.

Conventional RF systems are deployed in large packages that are designedto be mounted in a fashion that provides a stable and reproducibleelectromagnetic environment. PicoWINS devices are likely deployed in anundetermined orientation (with distribution on the surface or attachedto threats), and operate in variable environments (operating on asurface, attached to a target, etc.). Further, PicoWINS devices operatein conditions of exposure to rigorous environmental conditions includingshock, temperature excursion, and exposure to weather. Further, thePicoWINS electromagnetics affect the impedance matching requirements forPicoWINS receiver and transmitter systems.

A number of antenna configurations can be used in an embodiment,including but not limited to patch antennas, printed and modularlyattached dipoles and loops, and subresonant matched elements. A patchantenna with a rectangular shape simplifies the analysis of the fieldsunder the patch in that sinusoids are easily fit to the equivalentcavity boundary conditions. However, other shapes of patches such ascircular, triangular, or a variety of other shapes also provide resonantantennas. Resonant structures are appropriate for PicoWINS due to theirrelative insensitivity to the surrounding environment. Otherconfigurations, such as traveling wave or leaky wave antennas, may alsobe appropriate, particularly when combining communication antennas withan electromagnetic proximity sensor.

Of particular utility for PicoWINS is the annular ring geometry. In oneembodiment, it is implemented with a metalized ring of inner radius a,and outer radius b, on a dielectric substrate above a ground plane. Thisring can be modeled as a cavity with a perfect electric conductor (PEC)top and bottom and a perfect magnetic conductor (PMC) surrounding theexternal and internal radii. The resonance conditions for this structureare derived from the eigenfunctions resulting from the solution of thewave equation in cylindrical coordinates to match the PMC and PECboundary conditions. The primary reason for using the dielectric ring isthe reduction in size achieved for the lowest order mode as compared tothe square, circular, or triangular patch antenna, while providing highfield confinement, i.e., detuning insensitivity to the environment.

The dielectric ring antenna offers compact geometry with efficientradiation, and offers the prospect of a package producing a compact,sealed structure, as discussed herein. The properties of the dielectricring antenna show suitability for 2.4 and 5.8 GHz radiation. Diameter ofthe dielectric ring scales from approximately 2 cm to less than 1 cm forthese two frequencies, respectively, on Rogers 4005 substrates. Furthersize reductions by a factor of 1.5 may also be achieved using a ringantenna optimized on an aluminum substrate such as Rogers 5210.

The WINS NG and PicoWINS technologies of an embodiment support bi-staticsensing and position location. Compact tactical sensing systems rely onpassive seismic, acoustic, and infrared detection methods. These sensorchannels are supported by micropower sensing methods, and incorporatedinto micropower PicoWINS packages in some embodiments. However, activemethods that are compatible with low power operation are also ofinterest. Active sensors direct a beam of radiation (typically acoustic,infrared, or microwave) towards regions in which a threat is expected.Active sensors offer an advantage over passive devices in detectingthreats that are quiet (acoustically or electromagnetically) or movingat a slow rate. Active sensors typically operate in a monostatic modewhere a beam of radiation is delivered by a source and reflectedradiation from the environment is received by the source. Monostaticsensors suffer from the problem that without a natural barrier, orbackstop, any motion of an object in the distant field of view may yieldan alarm condition. This limits the applicability of these devices andmay lead to the requirements for expensive, large, installations.

To overcome this energy problem, an embodiment of a WINS NG, PicoWINS orhybrid WINS network provides a series of network bi-static sensors.Using this technology, the WINS network is exploited to create naturalradiation monitoring paths, forming a network of densely interlinkedeffective “trip wires” in the secured regions. Specifically, radiationmay be launched from a network node using infrared, acoustic, ormicrowave beams. This transmission includes a combined probe beam andsignal code for beam intensity control and propagation measurement. Thisenergy carrier is modulated in time so as to provide an identifying codecorresponding to the source. Then any other WINS node may receive thistransmission and detect changes in transmission loss. As such, activesource signal propagation and scattering is used to identify threats asbeam perturbations. This yields at least two benefits: the transmissionsignal to noise ratio is large, and network transmission and receptioneffectively terminate the beam and accurately define the propagationpath, eliminating the problems with undefined detection volume.

The PicoWINS of an embodiment includes a network bi-static sensingmethod based on low power acoustic transducers. Receiving nodes mayacquire the encoded signal and may derive frequency and time varyingtransmission path loss information. The acoustic transducers haveadditional benefit in the geolocation of the nodes. Bi-static sensingand network node relative geolocation are accomplished with amulti-level network of transmitting and receiving nodes. Low poweroperation relies on low duty cycle operation of the acoustic source. Inthe following scenario, the low-powered sensing receiver nodes arescattered through a given protection area. These nodes may haveenergy-constrained processing capabilities and no other positiondetermination methods or devices (for example, GPS). Relative nodegeolocation mapping may be completed using the following method.Absolute geolocation requires that one or more nodes carry GPS andcompass heading sensors.

In one network embodiment, PicoWINS nodes are scattered throughout aprotection area. Some members of this population carry acoustictransmitters. After dispersal, an area mapping mode is initiated forrelative geolocation of all the nodes with relation to a base-station,for example, a WINS NG node. After an RF sync pulse, a directionalultrasonic transmitter sweeps out a 360° arc. By knowing the time ofreception, a rough mapping of all nodes with respect to a base stationis achieved. Since the location of the base station is known, notaccounting for multi-path signals, a rough estimate of the node positionis easily ascertained relative to the base station. This method forms ananalog to the very high frequency omni-range radio (VOR) avionicsposition systems. By operating at low duty cycle, net energy cost islow. The use of acoustic ranging methods, instead of RF methods, relaxesrequirements for high processing speed synchronization and high energyfor time-of-flight measurement.

The use of multiple nodes, providing time-of-flight information forpropagation in both upwind and downwind directions, eliminates windspeed induced errors. This is analogous to the methods used for windspeed cancellation in the meteorological systems that provide directmeasurement of atmospheric temperature through high accuracy measurementof the speed of sound between two points. In another embodiment, theposition location methods discussed with reference to WINS NG might beemployed.

After geolocation has been completed, network bi-static sensing maycommence. In this case, the transmitter either continually sweeps, ortransmits in an omni-directional pattern, saturating the environmentwith a coded ultrasonic signal. Any threat moving through a detectionarea systematically trips a complex web of detection zones.

The network bi-static capability also provides a reference geolocationmap for moving PicoWINS nodes that attach to and move with threats. Inthis case, the threat location is determined relative to the PicoWINSnetwork.

Jamming of a WINS NG network by an opponent is readily detected andlocated because the transmission and mapping originate from the centerof the coverage area. Any attempt to flood with a jamming acousticsignal is seen as an error and results in an alarm condition. Becausethe relative sensor placement is known from the mapping sweep, a jammerthreat moving across the coverage area transmits its instantaneousposition and travel direction back to the network.

Operation algorithms ensure that all of the PicoWINS functionality iscontained in a compact, micropower system. In one embodiment, thePicoWINS nodes operate with low clock rate, fully static CMOS processortechnology. These processors, operating the state machine systemdescribed herein, use standby current of only a few microamps. Using theproper analog input circuit components as well as the proper sensorsystems, the PicoWINS node lies fully dormant until a sensor inputappears. In one operational mode, multiple PicoWINS nodes are scatteredover an area operating in a constantly vigilant, but deep sleep state.Vehicle and personnel detection rely on sensor systems with no biasenergy. For example, these may be the piezoelectric active substrates.The PicoWINS node remains dormant and is inaccessible to the networkuntil an event occurs. This operational mode provides the longestpossible operating life. With this mode, operating life approaches themany-year “shelf-life” of the battery sources.

As described herein, the PicoWINS nodes operate with low clock rate,fully static CMOS processor technology. These processors, operating WINSprotocols, use operating current of 20-30 microamps at the clock ratesused for PicoWINS operation. The PicoWINS processor operatescontinuously in a vigilant state, examining both sensor signals andnetwork operation. In this alternative operating mode, additional energythat is not required for sensor-triggered operation is supplied to thePicoWINS node communication system. While this operational mode resultsin increased energy dissipation, it provides connectivity to nodes thatmay continuously participate in network operation. The PicoWINS nodesremain accessible and may be reprogrammed in this state. Continuousaccessibility of the PicoWINS network allows a range of importantsurveillance capabilities. In other embodiments, both theevent-triggered and continuously vigilant modes can be combined foradditional behavior requirements.

In the event that a threat is detected, the node enters a state ofhigher power dissipation and joins a network, transmitting informationback to a central master gateway node. While the PicoWINS nodes havelimited transmit distances to extend device longevity, they cancommunicate their status and threat detection information throughout thenetwork using multihop communication methods.

An alternate communication method, which is implemented instead of or inconcert with other models, assumes the presence of occasional datacollection nodes. These may be at distributed gateways or at specificchoke points in the field. In this case, PicoWINS nodes saturate an areaof protection with various types of sensor systems. Vehicles, personneland other objects move through the active area, activating and pickingup the sensors. Information is then retrieved at the strategic chokepoints where nodes with longer transmit range and higher computationpower gather, sort, and make decisions on vehicle type or class,personnel, and direction of travel, past position and speed, as well asother parameters. In this protocol, the PicoWINS node function for mostof their lifetime in a sleep-mode consuming minimum power becausetransmission occurs only when a node is disturbed.

Hybrid WINS Networks

The modular structure of the WINS NG and PicoWINS devices, together withthe ability to self-assembly from networks to Web connections, enablescreation of mixed networks for a variety of purposes. Such hybridnetworks can be used for example in security, medical, industrial,military, residential, and vehicular applications on local,metropolitan, and global scales.

One type of mixed or hybrid network supported by embodiments of the WINSNG and PicoWINS technology includes mixed wired/wireless networks. Thereare a number of situations in which it is desirable for some fraction ofthe nodes to be connected to a wired network (e.g., to conserve energy),and other situations where dual communication modes are desirable (e.g.,for robustness against wires being cut). The calculations herein showhow a common baseband can be used for both the wireless and wiredcommunications for the ranges of interest, realizing cost savings in thedesign.

For a transmit antenna at height h_(t), receive antenna at height hr anddistance d from the transmitter, a radiated power of P_(t), transmit andreceive antenna gains of G_(t) and G_(r) respectively, and assuming aperfectly reflecting (and flat) earth, the received power P_(r) for thefar field is given by $\begin{matrix}{P_{r} = \frac{P_{t}G_{t}G_{r}h_{t}^{2}h_{r}^{2}}{d^{4}}} & {{Equation}\quad 1}\end{matrix}$

This simple two-ray model assumes no obstructions, absorption, ormultipath. The noise power at the receiver is given by N_(o)=kFTW, wherek=1.38×10-23 is Boltzman's constant, F is the noise figure of thereceiver, T is the temperature in Kelvin, and W is the bandwidth. Thus,the signal-to-noise ratio (SNR) may be simply evaluated.

For telephone wire, charts on the attenuation in dB for different gaugesof wires are provided in J. J. Werner, “The HDSL Environment,” IEEEJSAC, August 1991. Examples are as follows:

Frequency 19 gauge 22 gauge 26 gauge 10 kHz  2 4.5  8 100 kHz  4 8  14 1MHz  15 24  40 10 MHz  70 95 150 100 MHz 220 300 470

As an example, using the worst type of wire, signaling at 1 Mb/s at 10MHz using binary phase shift keying (BPSK), and assuming a very poornoise figure of 30 dB, a transmission range of more than 600 meters (m)can be achieved with less than 1 milliwatt (mW) transmitted power, and arange of more than 300 m can be achieved with less than one microwatt.Since the response of the channel is quite flat after the first hundredkHz, little or no equalization is required. Even at 100 MHz, for 100 mthe attenuation is only 30 dB. This compares to radio attenuation of 94dB, according to equation 1. This savings of 6 orders of magnitude inpower actually understates the benefit, since there are no upconversionor downconversion costs. Thus, for example, frequency hopped spreadspectrum may be used for the telephone wire, re-using all the basebandhardware from the radio and simply bypassing the upconversion andantenna matching circuitry when the wired connection is employed.

The situation changes for a 1 km range. Here the radio attenuation is134 dB vs. 294 dB for the wired connection. Spread spectrum might beavoided, instead simply using the lower portion of the frequencyspectrum (avoiding the first 20 kHz to obviate the need forequalization). No equalization is used in standard T1 lines. Such datarates are adequate even for collections of still images, provided modestefforts at compression are made.

Echoes are a significant problem with telephone wire, due to impedancemismatches on each end. However, since bandwidth is not at a premium insensor networks that perform significant signal processing at source,this may be simply handled by using time division duplex transmission.The same approach is used for radio transmission since radios are unableto transmit and receive at the same time on the same frequency.

Thus, the attenuation of telephone wire is small enough for the rangesof interest, and the same modulation strategy may be used as for radiotransmission. This enables sharing of the digital baseband hardwareamong the two situations. With splice boxes inserted along one longline, the multiple access situation for radio and wired are verysimilar, and the same networking protocol is appropriate with a furtherreduction in development effort and node cost.

In an embodiment, a sensor web is constructed from a heterogeneousinterlocking set of components including the PicoWINS and WINS NGdevices. The sensor web includes PicoWINS and WINS NG devices of anembodiment internetworked with each other in a plug and play fashion, inuser-selectable configurations which include a mix of wired and wirelesscouplings. Furthermore, these networks can interface with substantiallyany commonly available power supply or communications/computer network.

A number of scenarios are possible for the sensor web. In an embodiment,PicoWINS devices are strung together to make communications and poweravailable. This provides spatial antenna diversity, low energy costcommunications for beamforming or data fusion, and power distributionfrom solar to processing nodes, but is not so limited. Additionally, thenetwork can be wired into a WINS NG or similar higher-performance devicethat performs higher level signal processing, and provides a long rangeradio link, or an adapter card for communications via a wired network.Alternatively, a pure PicoWINS network terminates in a device having avoice band modem and a connector to the telephone network, or simply atelephone line connection to scavenge power.

Interconnections of an embodiment are made using telephone wire.Telephone wire is low cost, has simple terminations. There are telephoneconnections in residences and offices, and there are jacks in computersand palm-tops. Data rates can be quite large on short connections aswould be expected for PicoWINS and WINS NG networks alike, with verysimple baseband modulations. Moreover, it is designed to convey bothinformation and DC power, and comes in many varieties.

Some embodiments use specialized cable connections in order to providesupport for higher frequency antennas, smaller size, integrated ribbonsof sensing, communications, and power. For example, in an embodimentusing literal sensor webs that hang down over objects like trees thewires have a structural purpose. Connections providing enhancedadhesion, or a mechanical lock (e.g. a screw-in connection) may also beused. Moreover, an embodiment that provides robustness against cuts inthe wired network uses a short antenna from the improved connector somedistance along the wire (e.g. half wavelength). The radio may then beused to communicate with a neighboring node.

The sensor web of an embodiment includes, but is not limited to,PicoWINS devices, WINS NG nodes, power adapters, communication adapters,low-complexity communication line drivers, inter-device cables, cablesplitters, external interface cables, and a plug and play networkprotocol. The PicoWINS devices include sensors, energy storage devices,solar arrays, radios, signal processing, at least two wired ports, anddata storage devices. The WINS NG nodes include sensor, energy storagedevices, solar array, radios signal processing, at least two wiredports, and data storage devices as for PicoWINS, but with greatercapabilities including higher speed communication ports. The poweradapters include vehicle, line, and other battery voltages, and includestandard communication/power ports for the sensor web. The communicationadapters may be embedded in the WINS NG devices or may stand alone.

The inter-device cable includes structurally sound connectors andantenna port capabilities supporting interconnection options thatinclude a telephone wire core option and sensor cable options. Cablesplitters of an embodiment avoid the requirement for a large number ofports on PicoWINS nodes. The cable splitters may be passive or mayinclude processing for store and forward or aggregation and routingfunctions, and repeater functionality. Furthermore, the cable splitterscan enable varied interconnect cables in the same network. The externalinterface cable includes a sensor web connector on one end and astandard telephone jack on another end. It is noted however, that manystandard power/communication cable types are possible. The plug and playnetworking protocol supports a number of network functions via theordinary process of link discovery and termination. It accepts new wiredor wireless connections. Moreover, it deals with gaps by activatingradios and performing appropriate data reduction. Additionally, it dealswith consequences for database access with network change. The networkprotocol/database management functions are adaptable according to thecommunication resources available and the capabilities of the devices oneither end of a link, as for example in a WINS NG/PicoWINS connection.

The sensor web of an embodiment supports mixing and matching of numerousnetwork products as well as interfacing with numerous power orinformation sources. Mixed wired and wireless networks are supportedwith no manual configuration required by the user. Longer range wirednetworks are supported using communication adapters that includestandard high-speed communication devices for digital subscriber lines.A wide range of computers and communications devices may be integratedinto the network, with the sensor web appearing, for example, as anInternet extension.

The WINS technology of an embodiment also supports the coexistence ofheterogeneous communications devices. In wireless channels, the multipleaccess nature makes coexistence of radios with very different transmitpower levels and transmission speeds difficult. This is made morecomplicated by the likelihood of there being no central controller forchannel access, making policing of the access channels more difficult.One method is to make available universally understood control channelsfor negotiation of connections between different users. These users willthen ordinarily switch to other channels according to the highest commondenominator of their capabilities. These channels can also be used forscheduling of persistent channel access, or to set up a multi-castgroup. However, it is generally anticipated that users will not grabthese channels for data transport. To enforce this, the protocol timesout users or otherwise enforces transmission duty cycle limits throughbuilt-in programming operating in each node.

In addressing the problem of defining an access scheme that is robustwith respect to interference from users who may not be conforming to theprotocol, a WINS embodiment uses a combination of spread spectrumcommunications and channel coding. As a side benefit, this also providessome diversity. More sophisticated users can attempt transmission atmultiple power levels (gradually increasing to avoid excessiveinterference), but this is not required of all users. The rapidacquisition of the appropriate code phase is assisted by a gateway incharge that can transmit a beacon which carries the access channel phaseto everyone within range. Alternatively, absent a gateway, a number ofpre-selected channels can be used for exchange of synchronism messagesamong active users. New users eavesdrop on these channels and then gainadmission having learned the correct local phase.

As previously discussed herein, node position location is an importantconsideration. Additional position location approaches for use in hybridnetwork embodiments are now discussed. These approaches include mixednetworks of WINS NG and PicoWINS nodes, where the WINS NG nodes haveGPS.

One network scenario includes a dense network of PicoWINS nodes overlaidwith a network of WINS NG nodes, such that every PicoWINS node is withinradio range of at least four WINS NG nodes. Three levels of positionlocation accuracy are plausible: location within the convex hull definedby the nearest WINS NG nodes; refinement based on adjacency relations ofPicoWINS nodes; 1.5 m if perfect time difference of arrival (TDOA) isperformed, using 200 MHz bandwidth.

The simplest location method is based on energy levels. If WINS NG nodesdetermine their locations using GPS, then they can build a simple modelof the local propagation conditions using a series of transmissionsamong themselves. That is, with path gain G=αd^(−n), two independentpaths suffice to solve for α and n. This crudely calibrates the energy,so that other hearers can compensate. This information can be broadcastin the sounding signals from the WINS NG nodes after performance of thecalibration step. The PicoWINS nodes hearing the sounding signals canthen adjust according to the formula for loss to estimate the apparentrange. The sounding signal of course also includes Cartesiancoordinates, so that the PicoWINS node can solve a system of equationsto estimate its location.

This procedure suffers from several sources of inaccuracies. First, thetransmission loss depends on direction, since the topography isnon-uniform. Second, local shadowing may be very different for nodeseven in the same direction. Third, multipath fading may be quitebroadband. All of these can lead to large errors in apparent position.However, if the node can hear many WINS NG radios, some weightedaveraging can be performed so that the node has a good chance of atleast locating itself among the closest of these radios.

Accuracy can be improved with a high density of PicoWINS nodes that alsotransmit. In this case each node locates itself within the convex hullof its neighbors. Using these adjacency constraints, a distributediteration on the estimates of the positions of the nodes is performed.

TDOA methods are also possible, but here the clock drift can beproblematic. If both nodes performing ranging have perfect timing, theprocedure is as follows. Node 1 sends a transition-rich sequence. Node 2records the positions of the transitions and after a fixed delay sends asimilar sequence in response. Node 1 then repeats after a fixed delay,and iteration is performed. Averaging the time differences between thesame transitions over the sequence and subtracting the fixed delaysresults in an estimate of the time difference. Alternatively and morerealistically, an autocorrelation can be performed on the sequence toyield the peak. Peak positions are compared in successiveautocorrelations, more or less as in early/late gate timing recovery.Unfortunately, the clocks of both nodes will deviate from the truefrequency, both systematically and randomly. Multiple repetitions takecare of the random but not the systematic drifts. The systematic errorsover the round trip interval must be only a few nanoseconds for thedesired measurement accuracies. This means for example that ranging atshorter distances tolerates larger systematic errors, since the physicalround trip is shorter and the sequences can be shorter, because SNR ishigher. Thus, multiple ping-pong round trip measurements of shortduration are better than single long duration sounding pulses eventhough both have the same noise averaging properties.

Now, all WINS NG nodes in the neighborhood equipped with GPS can derivean accurate clock from the satellites. PicoWINS nodes can then derivetheir clock from the sounding transmissions of the WINS NG nodes so thatthe systematic drift can be kept small. Absent GPS, but with an accurateclock, one node can broadcast a timing beacon. This broadcast canalternate with sounding signals, so that even though all nodes possessonly one radio all nodes get slaved to the same clock. The appropriateduty cycle for clock beacon and sounding signals depends on the clockdrift rates and the SNR for the transmissions. In this way, positionaccuracy depends chiefly on the most reliable clock in the transmissionneighborhood (provided drifts are more or less random afteracquisition). Furthermore, nodes may be specially constructed for thispurpose having duty cycles adjusted based on the classes of nodesperforming ranging.

In a hybrid network embodiment, PicoWINS nodes or tags perform rangingusing beacon chirp pulses emitted by WINS NG class nodes. Using thistechnique the WINS NG nodes send out a sequence of pulses, with varyingintervals so that the wavefronts intersect at different places overtime. The pulses include some coding to identify themselves. Tags whichreceive signals from two different sources can use simple amplitudemodulation (AM)-style diode demodulators to record these wavefrontcoincidences, and then the WINS NG nodes can tell the tags theirpositions. This procedure produces good results when the WINS NG nodeshave accurate clocks, range among themselves, synchronize their clocks,and the tags have a means of hopping their information back to at leastone WINS NG node. It places minimal clock burdens on the tags.

Regarding ranging among the reference nodes, the master sends out apulse of duration TM, which a slave uses to derive its timing. There isa propagation delay of TP, and a short guard time TG before the slavesends a short response pulse of duration TR, whose length is set by theneed for a reasonable SNR at the correlator output in the master. Afterthe propagation and guard time delays, the master repeats its pulse.This is repeated as necessary to average over the noise, and thus TR canbe made quite short. Round trip time is determined by looking at thetime between autocorrelation peaks T=2TP+2TG+TR+TM. If the master'sclock is perfect, uncertainty in timing is determined by the rate ofdrift of the slave's clock over the interval TG+TR. For example, supposethe clock rate is 200 MHz, with drift of 200 Hz (1 part in 10⁶). Pulsewidths of 200 MHz can yield a distance resolution of 1.5 m, with chipsof 5 nanosecond (ns) duration. At 200 Hz, a drift of 1 ns will take 40chirps. This method can apply to either acoustic or RF transmission.

In an embodiment each tag can hear up to three nodes that send outranging pulses, for example, short chirps over the 200 MHz range. Tagslocated where pulses overlap in time determine that they are halfwaybetween the sources if the pulses were sent at the same time. Given anoffset in the launch time of the pulses, nodes at other locations canget the ranges to each node. Increasing the time duration of the pulsesincreases the area that hears a coincidence. By dithering the starttimes of pulses, the area between a set of nodes can be painted so thatevery tag gets coincidences for each pair. One node can be designated asthe master. After the other WINS NG nodes have learned their positions,they can subsequently keep their clocks synchronized by listening to thepulses launched by the master, and dither their launch times accordingto a schedule computed using to an algorithm known by all the nodes.

Now consider a tag which hears a pulse x(t) launched by node 1 and apulse y(t) launched by node 2. The received wave at the tag isr(t)=x(t)+y(t)=A cos(ω+θ₁(t))+B cos(ω+θ₂(t)). This can be demodulatedwith a standard AM diode (envelope) detector. Without loss ofgenerality, suppose B<A, and rewrite r(t) as:

r(t)=(A−B)cos(ωt+θ₁(t))+B(cos(ωt+θ₁(t))+cos(ωt+θ₂(t))).

Then apply the trigonometric identity cos Q cos R=½cos(Q−R)+½cos(Q+R) toobtain:

2 cos ωt cos θ₁(t)+2 cos ωt cos θ₂(t)−½[cos(ω−θ₁(t))+½cos(ω−θ₂(t))].

The term in brackets is essentially at the carrier while the terms outfront are two AM waves. The envelope detector will eliminate all termsnear the carrier frequency, leaving

2B[cos θ₁(t)+cos θ₂(t)].

Applying the same trigonometric identity, this is

4B cos θ₁(t)cos θ₂(t)−B cos(θ₁(t)−θ₂(t)).

The first term can be removed by further low pass filtering, leaving afrequency modulated sinusoid at the same strength as the weaker of thewaves that reach the tag. With chirps, the frequency difference isconstant, leading to a phase slope that linearly depends on the offsetin the perceived onset of the chirps. This leads to refinement ofposition, especially when the tag has a receiver that is tuned todetermine which wave arrived first (to resolve a two-fold ambiguity). Ifthe relative start times of the pulses are slowly changed, the positioncan be refined by choosing the coincidence event which produces thelongest and lowest-frequency baseband signal, even for tags having onlyenvelope detectors.

The ranging signals are not limited to chirps. For security purposes,chirps or tones could be hopped, with the time durations of the hops setso that reasonable coincidence distances result for the pulses. Thehopping could be done, for example, using direct digital frequencysynthesis (DDFS) to keep phase coherence. Each coincidence paints thebranch of a hyperbola closest to the node with the delayed signal. Thewidth of the hyperbola is equal to the pulse duration times the speed oflight, and thus a 50 ns pulse paints a hyperbola branch of width 15 m.Of course distance resolution can be increased by looking at thefrequency difference at baseband, and the time duration of thecoincidence. The position along the hyperbola is determined byconsidering coincidences from other pairs of transmitters. The time topaint a 30 m square region is not large for three or four source nodes,even if time offsets are designed to move less than a meter at a time,since we can launch new pulses every 100 ns (the time for the wavefrontto move 30 m).

Dense Security Networks

Network security applications prove to be difficult for typical networksolutions because of requirements such as very high detectionprobabilities with very low costs. Further, deliberate countermeasuresto the detection network will be attempted, and so it must be robust.One embodiment of the WINS technology that accomplishes the intendedtask is a heterogeneous and multiply-layered network. By presenting theopponent with a large number of means by which it can be sensed andtracked, design of countermeasures becomes extremely costly. A layered,but internetworked, detection system permits incremental deployment andaugmentation, providing cost efficiencies. Such an approach is useful inmany applications, for example tagging of assets or materials forinventory control, automation of logistics, automated baggage handling,and automated check-out at retail outlets.

The deployment of WINS NG systems in security network applications suchas defense applications for battlefield, perimeter, and base security,as well as civilian analogs such as campus, building, and residencesecurity takes advantage of the low-cost scalable WINS self-installingarchitecture. The WINS NG sensing elements are located in relativeproximity to potential threats and multiple nodes may simultaneously bebrought to bear on threat detection and assessment. This densedistribution, when combined with proper networking, enables multihop,short range communication between nodes. Together with the internalsignal processing that vastly reduces the quantity of data to transmit,the largest WINS power demand, communications operating power, isdrastically reduced. Furthermore, network scalability is enhanced. Theinternal layered signal processing allows low average power dissipationwith continuous vigilance, and more sophisticated processing on occasionto reduce false alarms and misidentifications.

Requirements of typical security network systems for constant vigilancehave yielded systems that operate at high continuous power dissipation.Attempts to reduce operating power have constrained development tonon-standard micropower platforms. These platforms are not scalable inperformance and do not support standard development tools. The WINS NGsystem, by contrast, not only supports standard development tools whilemaintaining low power operation, but also is plug and play from networkassembly to connections to databases operated using the web server andweb assistant. This enables remote reconfiguration and facilitates awide range of extra functionality for security networks.

FIG. 44 diagrams a security system 4400 using a WINS NG sensor networkof an embodiment. The sensor node 4402 includes seismic sensors 4404 atthe base and a sensor suite 4406 on a raised column, for example, for aperimeter defense application. The nodes are compact in volumepermitting many to be carried. The column supports imaging, passive IR,active and passive acoustic, active microwave, magnetic, and othersensors as needed. An active illuminator 4408 can also be carried. Solarphotovoltaics provide power along with secondary cells for indefinitelife. Low operating power permits extended operation without solarillumination.

FIG. 45 shows a deployment network architecture 4500 of a WINS NG sensornetwork of an embodiment. The WINS NG nodes 4502 are located at a smallseparation of approximately 30 meters. Small separation permits nodes4502 to operate at low power, and relaxes sensor sensitivityrequirements. Moreover, it enables shorter range sensing modes to beused, providing the signal processor with high SNR measurements frommany domains, which simplifies identification problems. The nodes 4502image their neighbors with passive or active elements, thus providingsecurity and redundancy. Both passive and bistatic sensing modes may beemployed.

FIG. 46 is a multihop network architecture of a WINS NG sensor networkof an embodiment. Nearest neighbor, low power multi-hop links 4602 aredisplayed as solid arrowhead lines. The multi-hop architecture providesredundancy 4604 (dashed, fine arrowhead lines) to hop to next nearestneighbors, or complete long links at high power and in emergencyconditions. The WINS NG network terminates at a gateway 4610. The WINSNG gateway 4610 provides protocol translation capability between WINS NGnetworks and conventional networks including those of existing securitysystems. Thus, the WINS NG network is backward compatible with existingsystems in a transparent fashion. Moreover, gateways 4610 provide waysto connect with wide area networks such as the Internet, so that networkresources such as databases and higher levels of computing can beemployed in the security task. Gateways further enable remote controland analysis of the sensor network.

The hardware layering of preprocessor/processor together with the set ofAPIs allows large adaptability. Variable resolution, variable samplerate, variable power dissipation, adaptive signal processing algorithms,and event recognition algorithms that respond to background noise levelare all examples of adaptability. The communication physical layer isalso adaptive, with variable transceiver power and channel coding.Further, it is responsive to queries in accordance with the localcapabilities and connectivity to the Internet or other externalnetworks. For example, data hold times, degrees of signal processing,readiness to communicate and engage in data fusion among nodes, and dataaggregation can all be set by user queries, with relative prioritiesrelated to the energy costs of communicating, the local storage andsignal processing resources, the available energy supply, and thepriority of a given task.

Threat assessment is currently implemented with single point, fragile,and expensive cameras operating at long range. These devices are limitedto line-of-sight coverage. The WINS NG system, in contrast, deliverscompact imaging sensors (camera volume less than 20 cm³) in existingsecurity system platforms or with new dedicated WINS NG imaging nodes.These imaging nodes can provide coverage of all areas between securitynodes and the regions beyond the next nearest neighbor node forredundancy. The image sensors are supported by the WINS NG node digitalinterfaces and signal processing for motion detection and othercapabilities. For example, they can be triggered by the other sensors toavoid continuous operation with its relatively costly signal processingand communication requirements. The image sensors may incorporate aninfrared or visible flash illuminator, operating at low duty cycle toconserve energy.

The WINS NG network is resistant to jamming. Conventional tacticalsecurity communicators support long range single hop links. While thisprovides a simple network implementation, it requires high operatingpower (1-10 W or greater), and expensive radios. In addition, thefrequency band is restricted for military use. Finally, the allowedoperating channels are fixed and narrow. Such links are both susceptibleto jamming, and not covert. By contrast, the WINS NG network isimplemented with multi-hop power-controlled communicators, and is thusinherently low-power. In addition, for an embodiment operating in the2.4 GHz ISM band, it uses 80 MHz of bandwidth with a frequency hoppedradio, providing a vast increase in jamming resistance. Thus the linksare both covert and jam-resistant.

Further protection is provided in an embodiment that provides shieldingthrough distribution of nodes in space, distribution in network routing,and distribution in frequency and time. Each method adds an independent,tall barrier to jamming. The WINS jamming barrier operates by:exploiting multi-hop communication for short-range links and using thesevere ground-wave RF path loss as a protective fence; employingmultiple decoy wireless channels to attract follower jammers to thedecoys, and dividing attention away from the information carryingchannels; and adapting communication and network protocols and routingto avoid jammers and to draw jamming power away from the WINS assets.

FIG. 47 shows an example of WINS NG system shielding by distribution inspace in an embodiment. A random distribution of nodes 4702 isdisplayed, as they may be deployed by various means. Each node 4702 isshown surrounded by a characteristic internode separation 4704. At thisreference internode separation 4704, the RF power level received fromthe node at this radius is assigned a reference value of 0 dB. A jammer4706 is also shown. From a jammer 4706 operating at the same power levelas the node 4702, RF path loss will yield the contours of constant powerat −20, −40 and −60 decibels (dB). These contours are drawn to scale forrange R, using path loss dependence of R⁻⁴. This figure graphicallyillustrates that by implementing multi-hop communication between nearestneighbors, only a few nodes in the immediate vicinity of a ground-basedjammer may be affected. Thus, distant jamming of the network willrequire very high power levels, making the jammer an immediately obvioustarget.

FIG. 48 shows an example of WINS NG system shielding by network routingin an embodiment. The random distribution of nodes 4802 is displayed, asthey might be deployed by various methods. Each node 4802 is shownsurrounded by a characteristic internode separation 4804. At thisreference internode separation 4804, the RF power level received fromthe node 4802 at this radius is assigned a reference value of 0 dB. Ajammer 4806 is also shown. Multi-hop communication protocols may providepaths 4808 through the network that avoid jammers 4806, exploiting thenatural RF path loss barrier. At the same time, the network protocolsmay mask this network link 4808 from the jammer 4806, preventing orspoofing the jammer's ability to detect or measure its effectiveness.

FIGS. 49A and 49B show an example of WINS NG system shielding bydistribution in frequency and time in an embodiment. The jammer can beexpected to broadcast in narrowband, broadband, and in follower modes.The WINS NG system uses a concept for decoy signals that distracts thefollower jammer using a method that is unique to distributed networksand exploits node distribution to draw the jammer away from links thatmust be protected. For example, transmission occurs on one channel, withone frequency-hopping pattern, shown in FIG. 49B, at high power. Thefollower jammer must select and jam this signal. In the process ofjamming this signal, the follower jammer effectively jams its ownreceiver. In addition, transmission occurs on one of many otherchannels, hopping with different schedules. In addition, the WINS NGsystem information carrying channels may be low power channels, whichare assigned lower priority by a jammer. In the worst case, the followerjammer may only remove some, but not all of the available bandwidth.

In operation, with reference to FIG. 49A, the follower jammer signal(dotted line) tracks a single node signal (solid line). Clearly, thejammer may be readily equipped to detect and jam this frequency-hoppedsignal. However, with reference to FIG. 49B, the jammer is exposed to adecoy signal (dotted line) that may appear to be carrying information,but is merely a decoy. The dashed line, a covert low power signal,carries WINS NG system information. A multiplicity of decoy andinformation bearing channels may continuously hop, change roles, andexecute random and varying routing paths. The jammer is exposed to manylayers of complexity.

Security networks may alternatively and usefully be constructed usingcombinations of WINS NG and PicoWINS technology. Consider the basicproblem of detection and tracking of personnel, in an environment thatmight also include heavy vehicles or other sources of sensorinterference. If sensors are placed very close together, then thepersonnel tracking problem becomes enormously simplified. Short rangesimply high SNR for the measurements, and more homogeneous terrainbetween the target and nearby sensors. This reduces the number offeatures required in making a reliable identification. Further, objectsto be detected are within the convex hull of the sensor array, enablingenergy-based tracking (as opposed to coherent methods such asbeamforming). The short range enables simplified detection andidentification algorithms because the targets are few in number andnearby, and the algorithms can specialize on personnel. Finally,identification confidence is high: magnetic, acoustic, seismic, IR allhave good performance, and data fusion is simplified.

Considering the tracking problem, three nodes near an object could useTDOA methods for seismic signals to estimate the object's location, orthe network could take the energy centroid for all nodes that report anSNR above a certain threshold. This amounts to orders of magnitude lesscost in terms of processing and data exchange compared to TDOA methods.Accuracy is not very high, but with such a dense dispersal of nodes itdoes not have to be high. For example, simply determining the node towhich the target is closest is adequate for essentially all militarypurposes. Thus, a dense deployment of fairly simple nodes does not implyreduced detection capability.

One cost issue is the complexity of the identification algorithms. Ifevery device has to identify every possible target, then the combinationof the most difficult identification problem and most difficultdetection problem determines cost. However, if each type of sensor isspecialized for particular targets, then many economies can be realized.Heavy vehicles can be detected with a much less dense network than isrequired for personnel; thus, in minefield lay-down there can be sensorsdesignated to deal with vehicles and others designated to deal withpersonnel. A different mix of sensors is appropriate for the twodifferent classes of target, with different requirements forcommunication range, and sophistication of signal processing.

At short range, IR is likely to have good field of view, and the othermodalities are all likely to have high SNR. The likelihood is high thatone target (or target type) will totally dominate the received signal.With high SNR and a few sensing modalities, a small number of featureswill suffice to make a detection decision on personnel.

The modularity and self-assembly features of the WINS NG and PicoWINSnodes allow convenient mixing of many different types of sensors andsensor nodes in a region, while the capability to download newprogramming enables tuning of signal processing algorithms to particulartypes of threats. In an embodiment of a detection network, a densenetwork of fixed WINS NG sensors is supplemented by an even denserdistribution of sticky tags, or PicoWINS nodes, in the same generalarea. These tags can be attached to moving articles in numerous ways,including magnetic attraction, adhesives, and burrs. They may themselvesbe active or passive, with an acoustic or RF resonant response. The tagsare supplemented by an activation and tracking network.

In one network embodiment, tags are randomly deployed, and are activatedwhenever a certain range of motion is experienced. These areinterrogated by higher power, more widely spaced devices, such as a WINSNG node designed specifically to track the location of moving tags. Thetags may be initially activated by passing through a personnel sensorfield. If for example the magnetic signature and footstep pattern iswithin range, there is confidence the target is a soldier, and thusworth tracking. Alternatively, the personnel detection network activatesa mechanical device which disperses tags towards the target (e.g.,spring launched, “bouncing betty”, low-velocity dispersal, etc.). Thetag includes at least one supplemental sensor that in one embodimentindicates whether the tags have attached to moving targets, targetswhich bear metal, and so forth, so that higher confidence is obtainedabout the type of target which has been tagged.

Tagging also simplifies tracking when the target moves outside the zoneof dense detectors. Vehicular tags could be active, emitting specificidentification sequences that are tracked at long range with aerial orhigh-powered ground stations. Personnel tags can be either active orpassive, but in any case are much more easily detectable at range thanthe acoustic, seismic, visual, or heat signatures of humans. The tagdispersal areas need not be coincident with a dense network of personneldetectors. If the tags have sufficient sensing means to determine thatthey are likely attached to a particular host, they can make a decisionto respond to queries or not to respond. Likewise, the devices thatquery may be distinct from the devices that listen for responses. Forexample, in one network embodiment, an aerial device with high poweremits the interrogation signal over a wide area and ground deviceslisten for the response. In this way, many lower cost receivers leverageone expensive asset. These receivers are cued to expect possibleresponses by a preliminary message from a drone, so that they do nothave to be constantly vigilant.

Tags can also bind sensor information to an object, whether theinformation is supplied by the tag, external sensors, the user, or somecombination of these things. The purpose is to be able to easily recoversome information about the object bearing the tag. Most typically,external means are used to identify the object being tagged, and theninformation is impressed on the tag (e.g. price tags or baggage). A tagreading system acquires information about the object that wouldotherwise be extremely difficult to obtain by mechanical sensoryexamination of the object without a tag. A tag without sensing can stillbe used as the item that provides the unique identifier for the objectit is attached to, in which case all sensor data collected by thenetwork can be properly correlated to make the identification.Alternatively, some of this information is stored on the tag itself.

The tag itself can include some sensors and signal processing, and playsan active role in the identification of the object, either alone or incombination with other tags and sensor nodes. Since the tag has physicalcontact with the object, very simple sensors and signal processingalgorithms lead to high confidence in identification. In this mode ofoperation, the tag starts with no idea about what the object is, and ateach step downloads new software from other nodes (possibly using theirinformation), or performs measurements and processing at their request.The tag thus does not need to carry much software, leading to reducedcost.

The tags placed in the environment determine if they are attached tofriendly personnel or vehicles. In one embodiment, at attachment thetags or nodes send out an identification friend or foe (IFF) beacon(short range) and deactivate in response to a positive reply. In analternative embodiment, they listen for the IFF being emitted by thehost for some period before deciding to activate. They may also becommanded not to activate if attached over some period of time bysignals sent from some individual such as a platoon leader. Eitherembodiment can inhibit activation or distribution of tags for somelimited geographic area and time span. Additionally, tags issue inhibitsignals to prevent multiple tags from being attached to one object, sothat it is more difficult for an enemy to clear a path through a regionwith a high density of tags.

The tags placed in an area do not have to be of the same type. Forexample, more capable tags can preferentially be induced to attach totargets whose tags indicate that the target is interesting. Thus,grippers or adhesives may become exposed when it is indicated aworthwhile target is coming close. The collection of tags on the objectmay become a powerful sensor network by the time several iterations ofthis process are complete. Cumulative exposure to tag and sensor richareas therefore provides a much greater than linear increase inprobability of a target being identified and tagged for long-rangetracking, or acquired in imaging systems.

For increased robustness, a sensor network that is subject to localizedphysical attack whether by human or other agents may be enabled to sowthe region of devastation with new tags so that targets can continue tobe tracked. The WINS NG and PicoWINS technology provides thisself-healing capability. A variety of launchers may be used to dispersesensor nodes, webs, or tags into regions that were previously covered bya sensor network, or initial deployment of sensors. This can besupplemented by an airdrop of new nodes. Network reconfigurability andself-organization technologies make this possible. Temporarily sprayingonly PicoWINS tags in the region gives a lower level of reliability, butis an economical means of covering a gap until a full mixed PicoWINS andWINS NG network can be re-deployed. If the physical attack is due topersonnel, this provides the added benefit of immediately tagging them.The sensor launchers may also serve secondary purposes such as deployingsmall webs or antennas to tree tops to provide longer range coverage toand over denied areas.

Such a network is maintainable at several levels of operation. Easilyarranged and low cost delivery of components assure some minimal levelof functionality, with the ability to go to higher levels offunctionality given more time and expense. A mix of reliance uponlocally cached resources and the possibility of calling in replacementsremotely (via artillery or air drops) is more robust againstcombinations of jamming and physical attacks. This ability is enabled inpart by the self-assembling nature of WINS networks, both with respectto physical networks and also complete applications.

To have both robustness and efficient operation, the combinedheterogeneous network needs both vertical and horizontal integration.That is, each layer is capable of functioning independently, but higherlayers are also able to communicate with and command lower layers formore efficient operation. Loss of these higher layers therefore degradesdetection/tracking capability, rather than eliminating it. Loss of alower layer may also impede detection tracking coverage, but does noteliminate it. When combined with a limited self-healing capability andan ability to request replacement components, the opponent is faced withthe need to destroy essentially all the diverse components over a fairlywide area in a short period of time. The resources required to do thisare easily detectable at long range.

Further, there is no reason to have all layers in all locations. Thedifferent detection layers can be deployed as circumstances warrant,increasing the density or variety of types of sensors over time. Thissaves costs, leads to improved functionality for old networks, changesthe priorities of old networks, and facilitates self-healing. Thus, itis not required that one big sensor do the complete job, nor that sensornetworks all have homogeneous components. Rather, in embodiments usingthe WINS technology, the system is a network of interacting networkswhich can interoperate for increased robustness and efficiency. Thisheterogeneity is accomplished using a layered set of APIs such thatcommunication among all nodes is accomplished at the highest commonlevel, with appropriate self-organization protocols for each level.

Asset Tracking and Management/Manufacturing

Conventional asset management technology is based on Radio FrequencyIdentification Device (RFID) tag systems. The applications forconventional RF tags and asset management systems are constrained,however, by the capabilities of the available technology. For example,low cost RFID tags provide only presence information and capability forrecording of limited data records. Typical RFID asset management systemsuse low cost tags and high cost interrogator instruments. Interrogatorinstruments typically require an antenna having dimension of 0.4-1 m.The maximum reading distance between the interrogator and the tagsvaries from 0.6 m for low cost tags, to 2 m for large, high cost tags.Interrogators are typically large, high power instruments. The batterypowered tag devices are interrogated with a high power instrument andreturn data with battery powered transmission. Read range extends toapproximately 3 meters. Conventional tags for use in vehicleidentification use large, high power, fixed base interrogators. Thus,conventional RFID tag systems have limited capabilities and requirelarge, expensive interrogators and antennae.

The low power networked sensors of the WINS NG and PicoWINS technologyare well suited to advance the art of RFID asset management. Anembodiment of an asset management system is implemented with intelligenttags in the form of WINS NG sensors or mobile PicoWINS tags, and lowcost portable, distributed interrogator gateways in the form of WINS NGgateways. The industries benefiting from the asset management sensortags include, but are not limited to, aerospace, airlines, apparel,beverages, glass, chemicals, construction, food, food services, forestand paper, health care, industrial equipment, mail and freight, metalproducts, mining, automotive, oilfield services, refining,pharmaceuticals, publishing, railroads, rubber and plastic, soap,cosmetics, and utilities.

In some embodiments, WINS NG and PicoWINS technology, includingcontinuous low power sensing and event recognition, is integrated withexisting RFID systems that have an installed base of RFID interrogators.In other embodiments, WINS devices form the complete system.

In one embodiment, the WINS tag is attached to or integrated with aproduct or shipping container. Unlike conventional devices, transmissionrange of the WINS tag is at least 10 meters. In addition, the tag isautonomous and operates continuously. Unlike conventional tags thatprovide only identification, the WINS tag provides continuous on-boardmeasurement. Thus the WINS tag may record time, time of passing awaypoint, and may carry data among waypoints.

Because the WINS tag is autonomous, it may continuously or periodicallysense status, for example, temperature, shock, vibration, motion, tip,light level, and package opening and closing, but is not so limited.Unlike conventional RFID technologies, the WINS NG tag uses only acompact, low power interrogator that may be networked locally, deployedin a distributed network, or deployed as an independent autonomous unit.For example, the WINS NG tag interrogator may communicate by wirelesslinks and may be distributed within a warehouse, shipping vehicle,loading dock, or processing facility. The autonomous interrogator nodemay monitor the progress of an asset and determine if a sensed conditionor progress timing is out of bounds, as per a programmed schedule ofsensed variable limits. The interrogator, or WINS NG gateway, mayinterface with standard LAN, telephony, or wireless resources.

FIG. 50 is an asset management architecture 5000 including the WINS NGor PicoWINS tags of an embodiment. In this architecture 5000, WINS tags,or nodes, are attached to assets or integrated into shipping containers.Assets 5002 are stored in at least one warehouse 5004. Any or all of theassets 5002 may have nodes, or RFID tags (not shown) attached to them.Within a warehouse environment, the nodes attached to the assets 5002are in network communication with gateway node devices, such as WINS NGgateway nodes 5006A and 5006B within the warehouse structure 5004. TheWINS NG gateway nodes 5006 may be variously autonomous or networked. Thepath for unstored assets 5008A-5008D (shown as a dotted line 5016)carries the asset 5008 past a series of WINS NG gateway nodes 5006, suchas autonomous gateways 5006C and 5006D. The gateways 5006C and 5006Dserve as waypoints providing time-of-arrival, routing, and destinationinformation for the asset in communication with it through a WINS nodeattached to the asset. The gateway autonomous interrogators such asgateways 5006C and 5006D are fixed asset base stations distributed atwaypoints along the asset path 5016. The gateway 5006 learns and recordsits position and creates a message. In various embodiments, the gateway5006 need not be networked and may be an independent device in thefield. The gateway 5006, in some embodiments, carries a GPS device toallow its location be recorded and transferred to a passing WINS tag.

Autonomous WINS gateways 5006 are free of any infrastructure. Absolutetime and location, either recorded or GPS determined, are available tothe gateways 5006. A gateway 5006 compares progress in shipping or in aprocess line with that expected. The status information may bedownloaded to any gateway 5006. Both presence information and sensedasset information are available. At the end of a trip, or at any point,the asset 5008 may encounter a distributed, networked gateway 5006. Hereasset information is made available. In addition, a low power, portablegateway 5006 may be used as a handheld device for interrogation ofassets.

The WINS tag is integrated or attached to a container or other asset. Incontrast to conventional RFID systems, the WINS node continuouslysamples and records changes in the asset status. In addition, itcontinuously seeks the presence of WINS NG gateway devices. The gatewaydevices may be networked by a multihop wireless network, or byconventional wireless or wired services. The gateways may be distributedand operate autonomously, providing waypoint time-of-arrival informationto be passed to the WINS NG node for later download. A WINS node maybroadcast emergency status information regarding either properties of anasset, delay in the progress of the asset to a destination, or theasset's misrouting.

The WINS NG gateway devices are networked, unlike conventionalinterrogators, and multiple gateway devices may attempt to acquire nodesor the same node within the same cell. The WINS nodes communicate withefficient codes carrying location, history, and sensor information. TheWINS node continuously senses, and operates with compact cells for alife of multiple years, through exploitation of the many energyconserving features of WINS NG and PicoWINS node and networkarchitectures. The WINS node may respond to queries generated by usersanywhere within the local or wide area network to which it is connected,and makes full use of the database technology previously described withreference to WINS NG networks.

The connection via gateways to the Internet enables the use of WINS NGservers, web assistants, and database technology to remotely monitor andcontrol the sensor network. For example, nodes may be embedded inrotating machine parts to sense vibration. In this case, the asset beingmonitored might be a large industrial pump, critical to factoryoperation. When an alarm condition exists, the node alerts the remotemonitor, which may then command reporting of more detailed information.Diagnostic or prognostic algorithms may be run on this more detaileddatabase, or a human expert may view the data from a remote locationusing standard Web browsing tools. In this way, problems may be quicklyidentified without the need for experts to travel to a remote site.Shut-downs can be commanded before significant damage takes place andappropriate replacement and repair procedures executed.

In another embodiment, the sensor network is used to examine flow ofgoods through a warehouse. The database includes information such aslocation of goods and the time spent in the warehouse. Analysis of thisdatabase can reveal patterns of storage time for particular categoriesof goods, allowing more efficient arrangements to be made with suppliersand customers.

The WINS tags can also be used for monitoring, including applications inmonitoring control processes, equipment, and operations. Distributedsensors and controls enable fundamental advances in manufacturing andcondition-based maintenance. While conventional sensor systems relyingon wireline communication systems suffer from costly installation andmaintenance and restricted mobility, these typical systems hindermodification or reconfiguration of facilities and equipment. This is aparticularly important problem in high volume production lines wheremaintenance downtime is prohibitively expensive.

The WINS NG and PicoWINS systems are low cost, mobile systems that coverthe entire enterprise. The WINS systems for manufacturing includesensors installed on rotating tool bits, machine tools, workpieces, andassembly machines. Measurements can be made of diverse parameters andoperations, including feed forces and vibration, stamping, joining, andother operations. The mobility offered by the WINS NG technology enablesoperations to be modified dynamically while minimizing downtime. Mobileworkers can monitor local operations and examine each operation througha WINS NG system measurement. Further, WINS NG condition-basedmaintenance provides large operating cost saving by allowing tool andsystem maintenance to be scheduled in advance of a failure. Furthermore,in the manufacturing environment, the WINS tags can be used as personaldevices, as used for example in applications including communication,workplace safety, workflow monitoring, control and verification, andhealth care.

Suppliers of capital equipment, for example large machine systems, mayacquire most or all of their profit from after-market service providedon their equipment. Consequently, maintaining a customer service accountwith both replacement parts and service is critical. Suppliers oftenfind that customers are turning away from their business and replacingtheir high-quality replacement parts with low quality (and lower price)components from competitors. For example, for high end equipment in theenergy industry, replacement part orders may total larger than $1million for a single case with component costs of $50,000 and greater.This loss of replacement subsystem business erodes the revenue for thecapital equipment supplier.

The WINS network of an embodiment provides desired monitoring functionsand also manages the capital equipment assets, by performing acontinuous inventory of the equipment. This WINS network systemincludes: the ability to monitor the condition of machinery, equipment,instruments, vehicles, and other assets as well as monitoring theequipment location; the ability to monitor the condition of machinery,equipment, instruments, vehicles, and other assets as well as monitoringthe inventory of components on these systems thereby making it possibleto determine that the components have originated at a specific supplierand have been installed at a particular time; a WINS network withsystems for electronically marking and verifying the presence ofcomponents; a WINS network having systems for notifying a remote userwhen components are installed on a system that do not meetspecifications or do not contain the electronic marking; a WINS networkthat uniquely identifies components that contain a mechanical featurethat generates a unique and identifiable vibration pattern; a WINSnetwork that uniquely identifies components by detecting anelectromagnetic signal generated by the rotation of the component; aWINS network that derives energy for powering the network from anelectromagnetic signal; a WINS network that derives energy for poweringthe network from unique mechanical fixtures located on moving equipment;a WINS network that enables the operation of equipment when positiveidentification of components is made and disables the equipmentaccording to a specified protocol when unidentified components arepresent; a WINS network that enables the operation of equipment when itis located in a specified location or set of locations, disables theequipment when it is located in a specified location or set oflocations, and disables the equipment according to a specified protocolwhen the equipment is removed; a WINS network that enables the operationof equipment when a specified service is performed and disables theequipment according to a specified protocol when the service has notbeen performed as per a specified protocol; a WINS network that enablesthe operation of equipment when certain remote signals or messages arereceived and disables the equipment according to a specified protocolwhen the signals or messages have not been received; a WINS network thatmeasures, records, and communicates operating parameters that areassociated with safe or unsafe operation of equipment or safe or unsafeactions by operators; a WINS network that combines any set ofcombination of the above capabilities; and a WINS network that combinesany set or combination of the above capabilities and is remotelyconfigurable in its protocols that determine the response to any set ofconditions.

Wireless Local Area Networks (LANs)

Many technologies are currently used to bring high-bandwidth servicesinto the home and office, including cable television (CATV) modems,digital subscriber lines (DSL) that use ordinary telephone lines,optical fiber to the “curb” supplemented by DSL drop to the home oroffice, and satellite communications. However, provision of wide-areahigh-speed wireless services is costly and likely to remain so for sometime to come, while there is already a large installed base of CATV andtelephone lines available for use. While these typical technologiesprovide high speed access to one or a few points within the home atrelatively low cost compared to wireless solutions, re-wiring within thefacility to bring such services to every room, however, remains costly.Consequently, wireless solutions are desired. Likewise, in businesses,there is often a wired local area network, for example an Ethernetnetwork, that provides high-speed connectivity in a substantial fractionof the offices. The WINS NG gateways provides an interfacing among thesewired communications services to provide wireless connectivity within aresidence, office, or industrial facility.

Home applications of the WINS technology include security networks,health monitoring, maintenance, entertainment system management, vehiclecommunications, control of appliances, computer networks, location andmonitoring of children and pets, and energy and climate management. Theself-assembly features, compact size, and efficient energy usage of WINSNG and PicoWINS networks enable low-cost retrofitting for this fullrange of applications. The modular design of the nodes enablesconfigurations that can interoperate with emerging consumer radionetwork standards such as Bluetooth or Home RF. Higher-speed protocolssuch as Bluetooth can be used to multihop information throughout aresidence and/or to a vehicle, while lower speed and less costlysolutions are adopted for a dense security network. Nodes with higherspeed radios can be coupled to a reliable power supply. The WINS NGserver and web assistant technology make possible the remote monitoringand control of these systems with standard tools, including archiving ofimportant data and provision of warnings to the current registeredcommunications mode of the users (e.g., pager).

Office applications of the WINS technology include computer and computerperipheral networks, location of objects, smart whiteboards/pens,condition based maintenance of both office and heavy equipment such asphotocopiers and elevators, security, health monitoring, and energy andclimate management. The same features of self-assembly, compact size,and efficient energy usage enable low-cost introduction of the WINSnetworks into the office environment. The WINS networks interface to thewired or wireless LAN by means of WINS NG gateways, and to the Internetor other wide area network. The WINS NG and web assistant technologyenable remote management of these assets. With the same tools, a usermanages assets in the home, vehicle, or office, representing a largesaving in learning time.

Wireless Metropolitan Area Networks (MANs)

Typically, wide area wireless networks are constructed using a cellulararchitecture, either formed by terrestrial base-stations which re-usechannels, or by the spot beams of satellites which likewise re-usechannels in different locales. In cellular systems, all communicationsflows back and forth between the remote users and the basestations, andnever directly between users. The basestations typically have access toa high-speed network using either wires or different frequencies thanthose used for communication with users. This wide area backbone networkmay in turn provide access to the Internet. The density of channelre-use depends on the density of basestations, and thus theinfrastructure cost scales roughly linearly with the desired re-usedensity of the channels, which is in turn driven by the number of usersand their expected bit rate requirements. Typically, cellular systemsare designed to provide a limited number of categories of service, forexample, voice and low-speed data, and to have a roughly uniform qualityof service within the coverage region. This results in a largeinfrastructure cost and delay in providing new cellular services.

An alternative architecture known as information service stations hasbeen proposed by D. J. Goodman, J. Borras, N. B. Mandayam, and R. D.Yates, “Infostations: A New System Model for Data and MessagingServices”, Proc. IEEE VIC '97, volume 2, pp. 969-973, May 1997. In thisarchitecture, services are available only in the vicinity ofbasestations, with no requirement for geographically contiguous regionsof coverage. This is an extension of the basic architecture used forcordless telephones, with the difference that users who roam betweenregions are permitted to communicate with any basestation within range.Examples of commercial usage include telepoint services such as CT-2.Such a network can be incrementally deployed, for example, beginning inregions with high expected usage and gradually being constructed toprovide greater coverage. It may also be combined with a wide-areacellular network, with the latter providing wide area coverage for lowdata-rate services, with high speed services available in the vicinityof the information service stations. For example, wide area pagingnetworks can function in a fashion complementary to telepoint networks,serving as the means for alerting telepoint users to make a call at thefirst opportunity.

The WINS NG and PicoWINS networks of an embodiment offer valuableextensions of capabilities for overlaid information service station andwide area networks. The capability to self-assemble into multi-hopnetworks enables denser re-use of channels than is implied by the fixedbasestation infrastructure. By processing data at the source andenabling data aggregation, many more nodes can be included in thenetwork. Moreover, the multihop communication can reduce powerrequirements and thus cost for most of the radios in the network, with asmaller fraction of the radios needed to communicate with thebasestations, which may for example include WINS NG gateways. Themultihop communications can use the same set of channels used in thewide area cellular network, or a different set.

The use of WINS NG gateways enables access to WINS Web servers anddatabase management tools, allowing remote configuration and control ofthe network, and making these resources available to the remote nodes.Further, these database tools can be used to more tightly manageoverlaid networks so that data is routed in the fashion desired by auser (e.g., to meet quality of service requirements or conserve energy).Both high-speed and low-speed services can be managed using the samedevice, reducing cost. Examples of services enabled with the WINS NG andPicoWINS technology include, but are not limited to, medicalinformatics, fleet management, and automatic meter reading.

The WINS NG and PicoWINS node, or sensor applications in medicalinformatics include patient monitors, equipment monitoring, andapplications in tracking, tagging, and locating. For example, visits bymedical professionals can be automatically captured and logged in apatient's medical history by means of a WINS network and the associateddatabase services. The WINS network can also be used in the clinicalenvironment, as well as for ambulatory outpatients in the home or evenat work. Furthermore, the WINS network enables a patient's family tomonitor the patient's condition from their home or office, or to monitorsick children from other rooms in a house, and supply appropriatealarms. The components of an embodiment of such a network are body LANs,information islands (home, work, hospital), wide area low-speednetworks, and web-based services to link the components.

In all medical applications, there must be a means of gatheringinformation about the state of the body. Typically, medical monitoringdevices have proprietary user interfaces, with little attempt to providecommonality across different classes of machines. This results in costlyinvolvement of medical professionals at every step of the examination.By contrast, WINS NG nodes can interface to such devices, performingsome combination of processing and logging of data via gateways to theInternet and thus to databases constructed for this purpose. The outputsof many different monitoring devices are linked in a database, leadingto improved fusion of data, and more complete histories of the state ofthe patient. The monitoring devices may be compact devices that directlyattach to the body. Embodiments of a PicoWINS network self-assemble andreport observations to a device with some combination of longer rangecommunications or mass storage capability (e.g., a WINS NG gateway). Inan alternative embodiment, the devices may be stand-alone devices, or amix of body network and stand-alone devices, which nonetheless arelinked in a self-assembling network.

The medical professional may additionally employ wireless personaldigital assistants, which become members of the WINS NG network, toannotate or view data. Such a network on its own is useful in home orclinical settings where connectivity to the Internet is made throughWINS NG gateways in concert with wide area wired or wireless networks.In one embodiment, the medical informatics network includes monitoringdevices, and wireless information islands that connect via the Internetto databases and other remote services. This simplifies collection ofmedical information from diverse sources, and enables far more completerecords to be assembled.

In another embodiment, the monitoring and database query devices alsohave access to a wide area wireless networks, either on their own or bymeans of gateways. This has several advantages for ambulatory patients.The body network continuously monitors the state and location of thepatient, and reports warnings to both the patient and medicalprofessionals as the situation warrants. Example warnings includewarnings for the patient to either cease a risky activity or forassistance to be summoned (e.g., for heart conditions). This may provideadditional mobility to patients who otherwise would need close on-sitesupervision by medical professionals, and allow patients to live fullerlives with limitations on their activities being individualized, ratherthan being based on conservative assumptions. The body networkcommunicates using short-range and low-power methods, so that eachpatient may have many sensors without the need for multiple long rangeradios. The wide area network allows priority messages to be sent evenwhen the patient is out of reach of high-speed connections. In theinterim, the body network can log histories, and wait to download thisinformation or receive new programming until the next occasion on whichit is in proximity to high-speed connections.

In a further application, the same technology is used for monitoringhealthy individuals who may be engaged in hazardous activities (e.g.,firefighting, warfare, high-risk sports, childhood), so that assistancecan be promptly summoned as needed, and with a prior indication of thestate of the patient.

Just as patients can benefit from a variety of devices monitoring statusand location, so can large vehicles and fleets of vehicles. A WINSnetwork can be retrofitted to monitor the state of key components, andthen to report results via gateways located, for example, in serviceareas. If supplemented with a lower-speed wide area network, breakdownsor other emergency conditions are reported, and tasks such assnow-removal monitored and controlled. Other functions such as dispatchof emergency vehicles are improved if locations of all vehicles arenoted, and travel times for different routes (according to time of day)have been logged in the database. Large transfers of data take placenear WINS NG gateways with wired or high-speed wireless Internet access,while command and control information flows through the low speednetwork, and vehicle component data flows through its local multi-hopnetwork.

In addition to medical applications and vehicle management, along-standing need exists for fixed wireless data solutions asreplacements for hard-to-read utility meters. While these metersrepresent only 10% of the 220 million meters presently in service, thecost of reading these meters is estimated to be as high as 50% of allmeter-reading costs. Utility companies realize that they can cut theiroperating costs and improve information collection capabilities byadopting automatic meter reading (AMR) technology.

Inherent to any AMR technology is a communications link. Current AMRsolutions utilize telephone dial-up, power lines, and low power RFtechnology in the 928-956 MHz band. Cellular, Personal CommunicationSystem (PCS), or specialized mobile radio (SMR) airlinks are moreflexible than dial-up or power line links, and they cover a broadergeographic area than the 928-956 MHz signals. Despite these comparativeadvantages, wireless communications carriers will not capture ameaningful share of the AMR market unless they quickly develop anddistribute an AMR solution. Present solutions either demand installationof costly new infrastructure or payment of high subscription fees tomake use of existing wireless infrastructure.

In contrast, a solution using combinations of PicoWINS and WINS NGtechnology described herein provides convenient network self-assembly,signal processing at source to reduce communications traffic,multi-hopping using a low density of longer range links, combinationwith WINS database and Web server technology for remote management ofthe network, and a simple path towards incremental deployment, in thatwide area wireless networks are not required. In another embodiment,wide-area low data rate wireless access is available, and WINS NGtechnology permits efficient usage through local multihopping, dataprocessing, and data aggregation. Further, WINS NG networks deployed inhomes for home networking or vehicle networking purposes can in oneembodiment also be shared for the meter reading applications, furtherreducing costs.

To the extent possible, wireless carriers can combine AMR with demandside management (DSM) capabilities using the WINS NG system of anembodiment, as sensing, signal processing, and control interfaces areall available. Local capabilities greatly diminish the communicationrequired to support the applications. The DSM applications enable autility company to better manage its aggregate energy requirementsthrough agreements with customers.

Design and Testing of Composite Systems

The provision of networked processors, sensors, and storage incomponents of systems can greatly reduce the cost of development andtesting of composite systems. As in security applications, appropriateinterfaces achieve an efficient network with any combination ofinformation processing, collection, and storage elements and without theintervention of a human operator. Such security systems are themselvesconcerned with data collection and processing, while also being acomposite structure. In other applications, the device being assembledmay not have a purpose directly related to processing of data about thephysical world, but may nevertheless benefit from being networked insuch a fashion.

For example, consider production of commercial aircraft, which requiresextremely high levels of sensor instrumentation in the design stages,and a very large number of sensors in the finished product to providefor safe operation and assist in maintenance. Conventionally, sensorsare individually wired for testing, and in general are in a verydifferent configuration in the final product. The final product requirescareful design of the conduits and connections for the sensors and, dueto weight considerations, the length of cable and thus the number ofsensors is limited. By contrast, using an embodiment of a WINS NG andPicoWINS wireless/wireline network capable of self-assembly, flexiblere-routing around failures, data aggregation, and re-prioritizationbased on maintenance history are greatly simplified both in initialtesting and for maintenance support. Moreover, the availability ofconvenient external interfaces to the Internet through WINS NG gatewaysaffords remote incorporation of data in databases, and remote control ofthe WINS network using the Web.

In more advanced applications, networks of autonomous or roboticcomponents constitute a system, for example, for dealing with hostilesituations such as in battlefield, space, or undersea applications, orthe many challenges posed by autonomous manufacturing. Design of asingle device that is self-sufficient in energy and also mobile istypically very costly. Current practice in industrial production is tocreate specialized devices for particular tasks, and coordinate them bya combination of human intervention and separate wired networks forenergy, communication, and control. By employing physical, software, andcommunications interfaces using the WINS NG and PicoWINS systemstechnology, any device so equipped that is brought into communicationrange becomes part of the system. The system automatically adjusts asnew components are added or old components removed. Further, by means ofWINS NG gateways, the database and other resources available through theInternet are accessed by the network and remote users may control thenetwork, for example, by using the WINS web assistant and servers.

Vehicle Internetworking

WINS NG technology provides the first low cost global vehicleinternetworking solution. Using embodiments of the wireless sensornetwork, individual vehicle systems are monitored, queried, and upgradedon a global scale. Internet services provide remote access that isintegrated into the operations of a vehicle manufacturer. Vehicleinternetworking provides benefits through the entire vehicle life cycle,including manufacturing, distribution, sale, fleet or individual ownerinformation, maintenance, regulatory compliance, and used vehicle salesinformation.

FIG. 51 is a diagram of a vehicle internetworking system 5100 of anembodiment. In the system 5100, vehicles 5114, 5116, and 5118 are eachmanufactured by a single manufacturer, although the invention is not solimited. The vehicle manufacturer installs WINS NG system components inthe vehicles to allow internetworking throughout the life of eachvehicle. Additionally, the WINS NG system can be installed as anaftermarket component. The manufacturer's existing informationtechnology systems 5106, including vehicle information servers 5120, areused to participate in the internetworking system. Through WINS NGgateways 5108 and 5110 and the Internet 5112, world wide web access tovehicle information servers is provided to vehicles 5114, 5116, and5118. Because communication in the internetworking system is two-way,vehicle information servers can also access individual WINS NG nodes,such as gateways 5108 and 5110, and individual vehicles. Vehicleinformation servers can also be placed in any location apart from themanufacturer's existing information technology systems 5106. Vehicleinformation server 5104 is an example of a vehicle information serverthat may exist at any location.

Computers accessing the world wide web, such as computer 5102, haveaccess to nodes, vehicles, and vehicle information servers. Computer5102 may, for example, be a home, office, vehicle dealer, or servicestation computer.

Communication between vehicles 5114, 5116, 5118 and any of the gatewaynodes, such as gateways 5108 and 5110, in one embodiment, isaccomplished through wireless methods that do not require universal highspeed services, but can include a mix of low-speed wide area connections(for example, cellular telephones and pagers) and high-speed short rangeconnections (for example, to WINS NG gateways). Communication betweengateways 5108 and 5110 and Internet 5112 is, in various embodiments,accomplished using wireless or wireline methods.

The WINS NG technology provides low cost, low power, compact intelligentnodes that are coupled to vehicle diagnostic ports. The WINS NG node canfor example communicate via the Federal Communications Commission (FCC)ISM-band spread spectrum channels. These channels, in addition toproviding robust communication, are unlicensed, thus eliminatingwireless access subscription fees. Power limitations prevent wide areacoverage, and so communication over such channels may optionally besupplemented by lower speed access over licensed channels. The WINS NGnodes link to local area WINS NG bidirectional gateways that accessInternet services via multiple channels. The WINS NG node manages thevehicle access port, logs and processes vehicle information, finds thelowest cost Internet connection permitted by application latencyconstraints, and immediately enables a wide range of valuable servicesat small incremental cost. Thus, for example, a node may processdiagnostic port data and transmit a reduced data set to a server if onlycellular communications are available during a time window or,application permitting, queue the data until an available WINS NGgateway connection comes into range.

The WINS NG information systems provide benefits at each stage of thevehicle life cycle. Many of these benefits derive from WINS NGmonitoring capability. However, additional valuable benefits also resultfrom the ability to upgrade remote vehicles, distributed at anylocation. This WINS NG application permits remote scheduled, verifiedupgrade and repair of digital system firmware. In addition toeliminating recalls that require firmware upgrade, this ultimately opensa new market for vehicles system upgrade products supplied through avehicle manufacturer or their designated agents.

Other WINS NG system benefits apply to vehicle manufacturer fleetcustomers who can track, locate, monitor, secure and control vehicles inrental and other operations while reducing personnel costs. Access tovehicle manufacturer customers through the life cycle results frompersonalized, web-based information services. These services includevehicle owner automated help-desk businesses that bring value to acustomer and provide information via conventional mail or world wideweb-based commerce solutions. The WINS NG vehicle internetworking alsoprovides marketing and business information such as sales informationand vehicle usage data that may be used in, for example, formulatingtargeted advertising.

The vehicle internetworking system of an embodiment includes, but is notlimited to, embedded WINS NG nodes, WINS NG gateways, WINS serverapplications, and WINS web assistants. As a complete, lasting solutionfor vehicle Internet access, embodiments provide connectivity throughoutthe life cycle of the vehicle. Connectivity begins in manufacturing andproceeds through testing, distribution, sales, field use, maintenance,recall upgrade, and used vehicle sales. Connectivity of an embodimentincludes: availability on a national scale; connectivity to vehicles inall environments where the vehicle will be found using common hardware;connectivity in indoor and outdoor environments; scalability such thatonly a limited number of transactions are used for access to vastnumbers of vehicles; local information processing services at thevehicle internetworking component that reduce the communication payloadusing reconfigurable systems; a single hardware component solution forvehicle and Internet access to eliminate requirements for distributionand deployment of multiple products; operation with a single nationalnetwork service provider without the requirement of region-by-regionnegotiation with subscriber service providers; robust operation withatomic transaction methods to enable deployment on vehicles usingavailable diagnostic port power sources; secure operation that providesprivacy and authentication; low component cost at both the vehicle nodeand the Internet access points; capability for rapid, low cost,after-market deployment of the connectivity solution; and, ability todeploy large (e.g., 100 kb-1 Mb) data sets at a high speed and low cost.

Data access to vehicles includes many measurement capabilities. TheOn-Board Diagnostics standards, OBD-I and OBD-II, provide access to awide range of parameters. The information that is derived from dataprocessing at the node is of even greater value than the actual OBD datasets. Because all vehicle data may be aggregated, regardless of where itis collected, new capabilities are enabled. For example, thecharacteristics of entire vehicle populations and histories areavailable through the full capability of data technology for informationrecovery.

WINS networking technology products and information systems of anembodiment related to Internet access to vehicle systems includeinformation technology products, Internet services (enterprise,national, and international) that aggregate all vehicle information, andInternet services for management and information recovery fromdistributed vehicle monitoring. Information technology products includedatabase systems that recover all needed data in a secure fashion fromvehicles that appear at any location within reach of a gateway.Information technology products further include database methods thatmigrate, in an atomic fashion, entire operating system (OS) and othersoftware components to remote, mobile vehicle systems. These databasemethods ensure that all vehicles in a population acquire and properlyproduce the authenticated data needed, and receive the new commands,control, entertainment information, and AutoPC software upgrades. Thedatabase system manages the predistribution of code and data to gatewayand gateway clusters in anticipation of the arrival of a specificvehicle or a vehicle that is a member of a class.

In one embodiment of the WINS NG system, customers are served by vehicleInternetworking access at low duty cycle. Specifically, continuousaccess is not required, but access occurs when a vehicle arrives near agateway. Gateways are at locations including refueling stops,intersections, railroad switch yards, maintenance stations, and loadingdocks. Gateway connectivity is convenient on a global scale.

Data payloads of an embodiment can be large, and can include lengthy anddetailed signal histories, large code components, and entertainment codeand information, but are not so limited. Occasional downloads can belarger than 1 Mbyte.

Users benefit from a node that is continuously active with bothmonitoring and recording of vehicle condition. Data downloads to avehicle, such as software updates, are beneficial. Preferably, a nodefalls within the proximity of a gateway for a download, but is notrequired to be immediately adjacent to a gateway.

Internet access to motor vehicle systems using the WINS NG system of anembodiment provides for the transfer and handling of data products,control products, mobile user services, operator information services,fixed base user services, fleet vehicle owner services, fleet operatorservices, vehicle vendor services, and security services. The dataproducts include: location; vehicle status; vehicle maintenanceinformation; vehicle component asset management; and shipping vehicleasset management interrogator services. The control products include:authorization; reconfiguration; and upgrade. Mobile user servicesinclude entertainment. The operation information services include:vehicle information; maintenance information; upgrade; reconfiguration;and software installation for vehicle PC and information systems.

The fixed base user services include: maintenance and vehicle historyinformation; vehicle purchase and usage history; and vehicle maintenanceservice negotiation services. The fleet owner services include:location; history; maintenance; and usage. The fleet operator servicesinclude: workflow; scheduling; efficiency; and energy use. The vehiclevendor services include: recall prediction; software upgrade; recallcost elimination; regulatory compliance methods; software/firmware salechannels; vehicle usage data; and warranty repair verification. Securityservices include: active vehicle identification; vehicle identificationcombined with WINS imaging and sensing; and vehicle identificationcombined with WINS Web services.

In one embodiment, the WINS NG vehicle internetworking architectureincludes a low cost WINS NG node mounted on the vehicle diagnostic port.The WINS NG node provides local intelligence for recording OBD datahistories and provides local event and data recognition capability. TheWINS NG node self-assembles its network with WINS NG gateways forbidirectional access. The WINS NG node is reconfigurable via the WINS NGnetwork for new capabilities, and the WINS NG node conveysreconfiguration and programming information to the EEC module. The WINSNG node also carries additional sensor capability. The WINS NG nodecommunicates via local wireless equipment to assets on board thevehicle, including the AutoPC. The WINS NG node communicates withhandheld displays at service stations and other areas to providepersonalized services.

The WINS NG gateway devices are deployed in environments includingassembly areas, distribution centers, shipping facilities, dealerships,maintenance centers, gas stations, and other locations. The WINS NGgateways provide services in support of the WINS NG nodes and capabilityfor local storage and forwarding of individual or collective vehicledata for upload or download from the vehicle.

In an alternate embodiment, the networking is supplemented with low-bitrate communication to wider area networks, to preserve connectivity forhigh priority messages even when gateways are not in range. This can beby means of a secondary radio within the WINS NG node, or by means ofanother radio accessed through the vehicular communication network.

In one embodiment, servers aggregate vehicle data and provide WINS NGnetwork management. Servers further provide Web and other Internetservices to the vehicle manufacturer for a full range of businessbenefits. For example, vehicles become automatically registered into thedatabase through the self-assembly features of WINS NG systems.Parameters of the data collection process for the processes available atthe OBD port can be selected at a web site, and viewed by both themanufacturer and the vehicle owner. Diagnostic and prognostic algorithmsare run using this data, and these algorithms may themselves commandchanges in the type of data being reported based on the probability of afault being detected.

If a repair is suggested, the diagnostic information is available toeach of the vehicle owner, repair shop, and manufacturer, and data takenafter the repair is accomplished is used to verify whether the actiontaken is effective. The record of repairs made to a fleet of vehicles isused by engineers at the manufacturer to assist in designing newversions of the vehicle, or in suggesting pre-emptive maintenance.Records of the effectiveness of repairs over a range of vehicles is usedby manufacturers to assess the quality of work being performed atdifferent shops. This may also be used to suggest improved repairprocedures for problems that arise in numerous vehicles.

Alternatively, such diagnostics and quality assessment services areperformed by a third party that is given access to the database ofperformance histories. Thus, convenient web access to a database of themaintenance history of individual vehicles and fleets of vehicles isvaluable to many parties, ultimately saving money and improvingprocedures and products. Furthermore, the ability of WINS NG networkservers to issue queries that also affect how the data is collectedleads to deeper investigations of priority events, without requiringthis level of detail across the whole fleet.

Automotive Multimedia Interface Consortium (AMI-C) Bus

The IDB-C is an auto industry standard being developed to run overcontrol area networks (CANs), thus providing approximately 125 kb/s in awired local area network. While it is being developed for the consumerapplications automotive multimedia interface (AMI), the architecture isalso suitable for many aspects of the separate Original EquipmentManufacturer (OEM) network for vehicle-critical operations. With theIDB-C, devices connected to the network contain a device that isresponsive to the base protocol that serves to schedule allcommunications in the network. Further, the responsive deviceautomatically shuts off communications from hosts that aremalfunctioning or otherwise not conforming to the network protocol. Thiscan be embedded in connectors, for example as embodiments of PicoWINSdevices, or in the devices that are to connect to the network. The basenetwork thus carries all control traffic, with the gateway used toresolve disputes over line access. The network also carries data trafficfor devices that do not require high-speed connections.

FIG. 52 is a WINS NG network 5200 of an automotive embodiment. A WINS NGnode 5202 bridges two components of the network, bus 5210 and bus 5212,with intelligent PicoWINS devices used as the Control Network Interface(CNI) devices 5204 that link consumer electronics 5206 to the bus 5212.The CNIs 5204 control access to the bus 5212, and can perform dataaggregation or other functions needed to reduce access traffic. They mayadditionally perform security/authentication functions. Communicationsto exterior networks for testing can be enabled through an additionalwired or wireless port 5214 not connected to either bus 5210 and 5212.This enables rapid download of data in both directions and reprogrammingof functions, for example by means of a WINS NG gateway and itsconnection to the Internet, WINS NG servers, and databases.

A number of embodiments of the WINS NG network enable more efficientusage of the bus. In one embodiment, more signal processing takes placeat the source (e.g. for sensors within a PicoWINS or WINS NG device) sothat processed rather than raw data is transferred around the network.Also, multiple low speed devices can be coupled on a separate physicalwire, with aggregation of their data at an interface device to the IDB-Cbus (e.g. a WINS device), thereby lowering the cost of theircommunications connectors.

In an alternate embodiment, the WINS NG node interfaces to a variety ofbuses and wireless networks, and there may be a number of WINS NGdevices to deal with networks of varying speeds. The WINS NG nodesperform such functions as routing, security, data processing, andmanagement of external communications so that application requirementsare met with the lowest cost. New applications may be downloaded usingthe external networks so that the system may be upgraded over the lifeof the vehicle. Further, later generation nodes and devices may be addedto the network as part of the upgrade, taking advantage of theself-assembly features of WINS NG systems. The layered processing andAPIs in WINS NG nodes can present a common interface to other devicesthat get added to the vehicle, hiding differences in the sensory andcontrol networks among different vehicle makes. Thus, the WINS NG systembecomes a universal socket by which devices are added to automotivenetworks. More broadly, the WINS NG gateways perform a similar functionin monitoring and controlling processes in the physical world.

The foregoing description of various embodiments of the invention hasbeen presented for purpose of illustration and description. It is notintended to limit the invention to the precise forms disclosed. Manymodifications and equivalent arrangements will be apparent.

What is claimed is:
 1. A sensor network comprising a plurality ofnetwork elements including at least one node of a first type and atleast one node of a second type coupled among an environment and atleast one client computer, wherein functions of the at least one node ofa first type and the at least one node of a second type are remotelycontrollable, wherein the at least one node of a first type includes atleast one sensor that receives at least one data type from theenvironment, wherein the at least one node of a second type providesnode information including node resource cost and message priority tothe plurality of network elements, wherein processing of the at leastone data type is distributed among the plurality of network elements inresponse to the node information; wherein the at least one node of asecond type includes at least one preprocessor coupled to at least oneprocessor and a plurality of application programming interfaces (APIs),the preprocessor operating on real-time processes, wherein the pluralityof APIs are coupled to control at least one of sensors, actuators,communications devices, signal processors, information storage devices,node controllers, and power supply devices, where the plurality of APIssupport remote reprogramming and control of the at least one device. 2.The sensor network of claim 1, wherein a first type of data manipulationis performed by the at least one node of a first type and a second typeof data manipulation is performed by the at least one node of a secondtype.
 3. The sensor network of claim 1, wherein the plurality of networkelements automatically organize in response to the node information,wherein the automatic organizing comprises automatically controllingdata transfer, processing, and storage among the plurality of networkelements.
 4. The sensor network of claim 1, wherein a plurality oflevels of synchronization are supported among different subsets of theplurality of network elements, wherein a first level of synchronizationis supported among a first subset of the plurality of network elements,wherein a second level of synchronization is supported among a secondsubset of the plurality of network elements.
 5. The sensor network ofclaim 1, wherein data processing is controlled using at least oneprocessing hierarchy, the at least one processing hierarchy controllingat least one of data classifications, data transfers, data queuing, datacombining, processing locations, and communications among the pluralityof network elements.
 6. The sensor network of claim 1, wherein theplurality of network elements are self-assembling, wherein search andacquisition modes of the at least one node of a second type search forparticipating ones of the plurality of network elements, wherein adetermination is made whether each of the participating ones of theplurality of network elements are permitted to join the sensor networkusing a message hierarchy, wherein the sensor network is surveyed atintervals for new nodes and missing nodes.
 7. The sensor network ofclaim 1, wherein the plurality of network elements are managed as adistributed and active database using a distributed resource managementprotocol, wherein the plurality of network elements are reused amongdifferent applications, wherein the network elements are used inmultiple classes of applications.
 8. The sensor network of claim 1,wherein the functions include data acquisition, data processingcommunication, data routing, data security, programming, and nodeoperation.
 9. The sensor network of claim 1, wherein the at least onenode of a first type includes at least one preprocessor coupled among atleast one state machine, at least one application programming interface(API), and at least one sensor.
 10. The sensor network of claim 1,wherein the plurality of APIs are layered.
 11. The sensor network ofclaim 1, wherein the plurality of APIs enable distributed resourcemanagement by providing network resource information and messagepriority information to the plurality of network elements.
 12. Thesensor network of claim 11, wherein information transfer among theplurality of network elements is controlled using a synchronismhierarchy established in response to the resource information andmessage priority information.
 13. The sensor network of claim 1, whereinthe at least one preprocessor performs at least one of data acquisition,alert functions, and controlling at least one operating state of the atleast one node, wherein the at least one processor performs at least oneof signal identification, database management, adaptation,reconfiguration, and security.
 14. The sensor network of claim 1,wherein data processing and data transmission are controlled in responseto a decision probability of a detected event.
 15. The sensor network ofclaim 1, wherein at least one operation is performed on the at least onedata type in response to parameters established by a user, the at leastone operation including at least one of energy detection, routing,processing, storing, and fusing.
 16. The sensor network of claim 15,wherein the routing, processing, storing, and fusing are performed inresponse to at least one result of the energy detection.
 17. The sensornetwork of claim 15, wherein routing comprises selecting at least onedata type for routing, selecting at least one of the plurality ofnetwork elements to which to route the selected data, selecting at leastone route to the selected at least one of the plurality of networkelements, and routing the selected at least one data type to theselected at least one of the plurality of network elements.
 18. Thesensor network of claim 15, wherein processing comprises selecting atleast one data type for processing, selecting at least one processingtype selecting at least one of the plurality of network elements toperform the selected at least one processing type, and transferring theselected at least one data type to the selected at least one of theplurality of network elements using at least one route trough the sensornetwork.
 19. The sensor network of claim 18, wherein the selection of atleast one processing type comprises determining at least one probabilityassociated with a detected event and selecting at least one processingtype in response to the at least one probability.
 20. The sensor networkof claim 18, wherein data processed in a plurality of nodes isaggregated for further processing by other nodes.
 21. The sensor networkof claim 18, wherein data processed by the at least one node isaggregated for reporting to at least one user.
 22. The sensor network ofclaim 15, wherein storing comprises selecting at least one data type forstorage, selecting at least one storage type, selecting at least one ofthe plurality of network elements to perform the selected at least onestorage type, and transferring the selected at least one data type tothe selected at least one of the plurality of network elements using atleast one route through the sensor network.
 23. The sensor network ofclaim 15, wherein fusing comprises a first node transmitting at leastone query request to at least one other node, wherein the first nodecollects data from the at least one other node in response to the atleast one query request and processes the collected data.
 24. The sensornetwork of claim 1, wherein the plurality of network elements support atleast one of wireless communications, wired communications, and hybridwired and wireless communications.
 25. The sensor network of claim 1,wherein the at least one node of a first type and the at least one nodeof a second type are coupled to the at least one client computer usingat least one of the plurality of network elements, wherein the pluralityof network elements includes at least one gateway, at least one server,at least one network, at least one repeater, and at least oneinterrogator, wherein the at least one network includes wired networks,wireless networks, and hybrid wired and wireless networks.
 26. Thesensor network of claim 25, wherein the at least one network comprisesat least one of the Internet, local area networks, wide area networks,metropolitan area networks, and information service stations.
 27. Thesensor network of claim 26, wherein internetworking among the pluralityof network elements provides-remote accessibility using World WideWeb-based tools to data, code, management, and security functions,wherein data includes signals and images, wherein code includes signalprocessing, decision support, and database elements, and whereinmanagement includes operation of the at least one node and the sensornetwork.
 28. The sensor network of claim 25, wherein the at least onegateway performs at least one of protocol translation, management of theplurality of network elements, management of communications with a leastone remote user, management of communications with at least one localuser, and interfacing with at least one communication physical layerincluding wired local area networks, packet radio, microwave, optical,wireline telephony, cellular telephony, and satellite telephony.
 29. Thesensor network of claim 1, wherein the plurality of network elementsfurther comprise at lest one database, wherein the at least one databaseincludes at least one of storage devices coupled to at least one of theplurality of network elements and storage devices separate from theplurality of network elements.
 30. The sensor network of claim 29,wherein cooperative sensing uses information of the at least onedatabase to provide non-local event correlation.
 31. The sensor networkof claim 29, wherein the at least one database comprises data-drivenalerting methods that recognize conditions on user-defined datarelationships including coincidence in signal arrival, node powerstatus, and network communication status.
 32. The sensor network ofclaim 29, wherein the at least one database is implemented in small footprint databases at a level of the at least one node of a second type andin standard query language (SQL) database systems at a level of at leastone server.
 33. The sensor network of claim 1, wherein the at least onenode of a second type includes sensing, processing, communications, andstorage devices supporting a plurality of processing a protocol layers.34. The sensor network of claim 1, wherein at least one redundantinformation pathway is established among the plurality of networkelements.
 35. The sensor network of claim 1, wherein the plurality ofnetwork elements comprise a plurality of network element sets, whereinthe plurality of network element sets are layered.
 36. The sensornetwork of claim 1, wherein a first network having a first node densityis assembled using the at least one node of a first type, wherein asecond network having a second node density is assembled using the atleast one node of a second type, wherein the second network is overlayedonto the first network.
 37. The sensor network of claim 1, wherein codeand data anticipated for future use are predistributed through thesensor network using low priority messages, wherein the code and thedata are downloadable from at least one location selected from a groupconsisting of storage devices of the plurality of network elements, andstorage devices outside the sensor network.
 38. The sensor network ofclaim 1, wherein data is transferred using message packets, wherein themessage packets are aggregated into compact forms in the at least onenode using message aggregation protocols, wherein the messageaggregation protocols are adaptive to at least one feature selected froma group consisting of data type, node density, message priority, andavailable energy.
 39. The sensor network of claim 38, wherein themessage packets include decoy message packets, wherein information to betransferred is impressed on random message packets to providecommunication privacy.
 40. The sensor network of claim 1, wherein the atleast one node of a first type and the at least one node of a secondtype include at least one of seismic, acoustic, infrared, thermal,force, vibration, pressure, humidity, current, voltage, magnetic,biological, chemical, acceleration, and visible light sensors.
 41. Thesensor network of claim 1, wherein at least one of the plurality ofnetwork elements determines a position of at least one other of theplurality of network elements.
 42. The sensor network of claim 1,wherein software is transferable among the plurality of networkelements, wherein the software transfer is remotely controllable. 43.The sensor network of claim 1, wherein at least one public key securityprotocol is used to protect communications.
 44. The sensor network ofclaim 1, wherein the at least one node of a second type includes aGlobal Positioning System device providing location and timeinformation.
 45. The sensor network of claim 44, wherein the at leastone node of a second type assists the at least one node of a first typein determining a position of the at least one node of a first type. 46.The sensor network of claim 1, wherein the plurality of network elementssupport short range and long range communications.
 47. The sensornetwork of claim 1, wherein the at least one node of a first type andthe at least one node of a second type comprise at least one of sensornodes, gateway nodes, thin film substrate sensor nodes, tag nodes,conformal nodes, wired nodes, wireless nodes, personnel nodes, equipmentnodes, and vehicle internetworked nodes.
 48. A sensor network comprisinga plurality of network elements including a plurality of node typescoupled among an environment and at least one client computer via atleast one coupling with the Internet, wherein functions of the pluralityof node types are remotely controllable and the plurality of node typesare programmable via internetworking among the plurality of networkelements, wherein the plurality of node types includes first and secondnode type, wherein the first node type includes at least one sensornode, wherein the second node types include at least one preprocessorcoupled to at least one processor and a plurality of applicationprograming interfaces (APIs), wherein the preprocessor operates onreal-time processes, wherein the plurality of APIs are coupled tocontrol at least one device that includes at least one of sensors,actuators, communications devices, signal processors, informationstorage devices, node controllers, and power supply devices, wherein theplurality of APIs support remote reprogramming and control the devices.49. The sensor network of claim 48, wherein at least one node type ofthe plurality of node types provides node information including noderesource information and message priority to the plurality of networkelements, wherein data processing is distributed among the plurality ofnetwork elements in response to the node information.
 50. The sensornetwork of claim 49, wherein the plurality of network elementsautomatically organize in response to the node information, wherein theautomatic organizing comprises automatically controlling data transfer,processing, and storage within the sensor network.
 51. The sensornetwork of claim 48, wherein the plurality of network elements comprisea plurality of network element sets, wherein the plurality of networkelement sets are layered.
 52. The sensor network of claim 48, whereincode and data repredistributed to the plurality of network elementsusing low priority messages, wherein the code and the data aredownloadable from at least one of storage devices of the plurality ofnetwork elements, and storage devices outside the sensor network. 53.The sensor network of claim 48, wherein a plurality of synchronizationlevels are supported among different subsets of the plurality of networkelements.
 54. The sensor network of claim 48, wherein data processing iscontrolled using at least one processing hierarchy, the at least oneprocessing hierarchy controlling at least one of data classifications,data transfers, data queuing, data combining, processing locations,communications among the plurality of network elements.
 55. The sensornetwork of claim 48, wherein a first node type of the plurality of nodetypes includes at least one preprocessor coupled among at least onestate machine, at least one application programing interface (API), andat least one sensor.
 56. The sensor network of claim 48, wherein theplurality of network elements control data processing and data transferin response to a probability of a detected event in the environment. 57.The sensor network of claim 48, wherein the plurality of networkelements are self-assembling, wherein search and acquisition modessearch for participating ones of the plurality of network elements,wherein a determination is made whether each of the participating onesof the plurality of network elements are permitted to join the sensornetwork using a message hierarchy, wherein the sensor network issurveyed at random intervals for new nodes and missing nodes.
 58. Thesensor network of claim 48, wherein the sensor network is managed as adistributed and active database using a distributed resource managementprotocol, wherein the plurality of network elements are reused amongdifferent applications, wherein the plurality of network elements areused in multiple classes of applications.
 59. The sensor network ofclaim 48, wherein data is collected by the plurality of node types,wherein at least one operation is performed on the data in response toparameters remotely established by at least one user, the at least oneoperation including at least one of energy detection, routing,processing, storing, and fusing.
 60. The sensor network of claim 59,wherein routing comprises selecting at least one data type for routing,selecting at least one of the plurality of network elements to which toroute the selected data, selecting at least one route to the selected atleast one of the plurality of network elements, and routing the selectedat least one data type using the selected at least one route.
 61. Thesensor network of claim 59, wherein processing comprises selecting atleast one data type for processing, selecting at least one processingtype, selecting at least one of the plurality of network elements toperform the selected at least one processing type, and transferring theselected at least one data type to the selected at least one of theplurality of network elements using at least one route among theplurality of network elements.
 62. The sensor network of claim 59,wherein storing comprises selecting at least one data type for storage,selecting at least one storage type, selecting at least one of theplurality of network elements to perform the selected at least onestorage type, and transferring the selected at least one data type tothe selected at least one of the plurality of network elements using atleast one route through the plurality of network elements.
 63. Thesensor network of claim 59, wherein fusing comprises a first nodetransmitting at least one query request to at least one other node,wherein the first node collects data from the at least one other node inresponse to the at least one query request and processes the collecteddata.
 64. The sensor network of claim 48, wherein software istransferable among the plurality of network elements, wherein thesoftware transfer is remotely controllable.
 65. A sensor networkcomprising a plurality of network elements including a plurality of nodetypes, including at least one sensor node, coupled among at least oneenvironment coupled among at least one environment and at least oneuser, wherein the plurality of network elements are remotelycontrollable by the at least one user, wherein at least one node type ofthe plurality of node types provides node information including noderesource cost and message priority to the plurality of network elementsin response to at least one parameter of a signal received from the atleast one environment, wherein at least one function of the plurality ofnetwork elements is controlled in response to the node information,wherein at least one node type includes at least one preprocessorcoupled to at least one processor and a plurality of applicationprogramming interfaces (APIs), wherein the preprocessor operates onreal-time processes, wherein the plurality of APIs are coupled tocontrol at least one device that includes at least one of sensors,actuators, communications devices, signal processors, informationstorage devices, node controllers, and power supply devices, wherein theplurality of APIs support remote reprogramming and control the devices.66. The sensor network of claim 65, wherein the at least one parameteris remotely programmed by the at least one user.
 67. The sensor networkof claim 65, wherein the at least one function includes at least one ofprogramming, configuring, assembling the plurality of network elements,distributing processing among the plurality of network elements,establishing communication paths among the plurality of networkelements, selecting at least one mode of communication among theplurality of network elements, distributing data among the plurality ofnetwork elements, storing data, organizing at least one subnetwork amongthe plurality of network elements, controlling synchronization among theplurality of network elements, assembling data products, and reporting.68. A sensor network comprising: means for coupling a plurality ofnetwork elements including a plurality of node types among at least oneenvironment and at least one user, wherein the plurality of node typesincludes first and second node types, wherein the first node typeincludes at least one sensor node, wherein the second node types includewherein the second node types include at least one processor coupled toat least one processor and a plurality of application programminginterfaces (APIs), wherein the preprocessor operates on real-timeprocesses, wherein the plurality of APIs are coupled to control at leastone device that includes at least one of sensors, actuators,communications device signal processors, information storage devices,node controllers, and power supply devices, wherein the plurality ofAPIs support remote reprogramming and control the devices means forremotely controlling at least one function of the plurality of nodetypes; means for collecting data from the at least one environment usingthe first node type; means for providing node resource information fromthe second node type to the plurality of network elements; and means fordistributing storage and processing of the collected data among theplurality of network elements in response to the node information.